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is-small is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Tandon%2C+R&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Tandon%2C+R&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Tandon%2C+R&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14424">arXiv:2411.14424</a> <span> [<a href="https://arxiv.org/pdf/2411.14424">pdf</a>, <a href="https://arxiv.org/format/2411.14424">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Learning Fair Robustness via Domain Mixup </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhong%2C+M">Meiyu Zhong</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14424v1-abstract-short" style="display: inline;"> Adversarial training is one of the predominant techniques for training classifiers that are robust to adversarial attacks. Recent work, however has found that adversarial training, which makes the overall classifier robust, it does not necessarily provide equal amount of robustness for all classes. In this paper, we propose the use of mixup for the problem of learning fair robust classifiers, whic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14424v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14424v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14424v1-abstract-full" style="display: none;"> Adversarial training is one of the predominant techniques for training classifiers that are robust to adversarial attacks. Recent work, however has found that adversarial training, which makes the overall classifier robust, it does not necessarily provide equal amount of robustness for all classes. In this paper, we propose the use of mixup for the problem of learning fair robust classifiers, which can provide similar robustness across all classes. Specifically, the idea is to mix inputs from the same classes and perform adversarial training on mixed up inputs. We present a theoretical analysis of this idea for the case of linear classifiers and show that mixup combined with adversarial training can provably reduce the class-wise robustness disparity. This method not only contributes to reducing the disparity in class-wise adversarial risk, but also the class-wise natural risk. Complementing our theoretical analysis, we also provide experimental results on both synthetic data and the real world dataset (CIFAR-10), which shows improvement in class wise disparities for both natural and adversarial risks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14424v1-abstract-full').style.display = 'none'; document.getElementById('2411.14424v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12127">arXiv:2411.12127</a> <span> [<a href="https://arxiv.org/pdf/2411.12127">pdf</a>, <a href="https://arxiv.org/format/2411.12127">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Statistics Theory">math.ST</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Fine-Grained Uncertainty Quantification via Collisions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Friedbaum%2C+J">Jesse Friedbaum</a>, <a href="/search/cs?searchtype=author&query=Adiga%2C+S">Sudarshan Adiga</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12127v1-abstract-short" style="display: inline;"> We propose a new approach for fine-grained uncertainty quantification (UQ) using a collision matrix. For a classification problem involving $K$ classes, the $K\times K$ collision matrix $S$ measures the inherent (aleatoric) difficulty in distinguishing between each pair of classes. In contrast to existing UQ methods, the collision matrix gives a much more detailed picture of the difficulty of clas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12127v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12127v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12127v1-abstract-full" style="display: none;"> We propose a new approach for fine-grained uncertainty quantification (UQ) using a collision matrix. For a classification problem involving $K$ classes, the $K\times K$ collision matrix $S$ measures the inherent (aleatoric) difficulty in distinguishing between each pair of classes. In contrast to existing UQ methods, the collision matrix gives a much more detailed picture of the difficulty of classification. We discuss several possible downstream applications of the collision matrix, establish its fundamental mathematical properties, as well as show its relationship with existing UQ methods, including the Bayes error rate. We also address the new problem of estimating the collision matrix using one-hot labeled data. We propose a series of innovative techniques to estimate $S$. First, we learn a contrastive binary classifier which takes two inputs and determines if they belong to the same class. We then show that this contrastive classifier (which is PAC learnable) can be used to reliably estimate the Gramian matrix of $S$, defined as $G=S^TS$. Finally, we show that under very mild assumptions, $G$ can be used to uniquely recover $S$, a new result on stochastic matrices which could be of independent interest. Experimental results are also presented to validate our methods on several datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12127v1-abstract-full').style.display = 'none'; document.getElementById('2411.12127v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.12309">arXiv:2410.12309</a> <span> [<a href="https://arxiv.org/pdf/2410.12309">pdf</a>, <a href="https://arxiv.org/format/2410.12309">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Correction to Local Information Privacy and Its Applications to Data Aggregation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+B">Bo Jiang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.12309v1-abstract-short" style="display: inline;"> In our previous works, we defined Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies epsilon-LIP for any epsilon>0 for arbitrary Px. However, this claim is not completely correct. In this document, we provide a correction to the valid range of privacy parameters of our previously… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12309v1-abstract-full').style.display = 'inline'; document.getElementById('2410.12309v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.12309v1-abstract-full" style="display: none;"> In our previous works, we defined Local Information Privacy (LIP) as a context-aware privacy notion and presented the corresponding privacy-preserving mechanism. Then we claim that the mechanism satisfies epsilon-LIP for any epsilon>0 for arbitrary Px. However, this claim is not completely correct. In this document, we provide a correction to the valid range of privacy parameters of our previously proposed LIP mechanism. Further, we propose efficient algorithms to expand the range of valid privacy parameters. Finally, we discuss the impact of updated results on our original paper's experiments, the rationale of the proposed correction and corrected results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.12309v1-abstract-full').style.display = 'none'; document.getElementById('2410.12309v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.06339">arXiv:2410.06339</a> <span> [<a href="https://arxiv.org/pdf/2410.06339">pdf</a>, <a href="https://arxiv.org/format/2410.06339">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+W">Wenhan Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhong%2C+M">Meiyu Zhong</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Krunz%2C+M">Marwan Krunz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.06339v1-abstract-short" style="display: inline;"> Deep Neural Network (DNN) based classifiers have recently been used for the modulation classification of RF signals. These classifiers have shown impressive performance gains relative to conventional methods, however, they are vulnerable to imperceptible (low-power) adversarial attacks. Some of the prominent defense approaches include adversarial training (AT) and randomized smoothing (RS). While… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06339v1-abstract-full').style.display = 'inline'; document.getElementById('2410.06339v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.06339v1-abstract-full" style="display: none;"> Deep Neural Network (DNN) based classifiers have recently been used for the modulation classification of RF signals. These classifiers have shown impressive performance gains relative to conventional methods, however, they are vulnerable to imperceptible (low-power) adversarial attacks. Some of the prominent defense approaches include adversarial training (AT) and randomized smoothing (RS). While AT increases robustness in general, it fails to provide resilience against previously unseen adaptive attacks. Other approaches, such as Randomized Smoothing (RS), which injects noise into the input, address this shortcoming by providing provable certified guarantees against arbitrary attacks, however, they tend to sacrifice accuracy. In this paper, we study the problem of designing robust DNN-based modulation classifiers that can provide provable defense against arbitrary attacks without significantly sacrificing accuracy. To this end, we first analyze the spectral content of commonly studied attacks on modulation classifiers for the benchmark RadioML dataset. We observe that spectral signatures of un-perturbed RF signals are highly localized, whereas attack signals tend to be spread out in frequency. To exploit this spectral heterogeneity, we propose Filtered Randomized Smoothing (FRS), a novel defense which combines spectral filtering together with randomized smoothing. FRS can be viewed as a strengthening of RS by leveraging the specificity (spectral Heterogeneity) inherent to the modulation classification problem. In addition to providing an approach to compute the certified accuracy of FRS, we also provide a comprehensive set of simulations on the RadioML dataset to show the effectiveness of FRS and show that it significantly outperforms existing defenses including AT and RS in terms of accuracy on both attacked and benign signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06339v1-abstract-full').style.display = 'none'; document.getElementById('2410.06339v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IEEE Milcom 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19060">arXiv:2409.19060</a> <span> [<a href="https://arxiv.org/pdf/2409.19060">pdf</a>, <a href="https://arxiv.org/format/2409.19060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> CURATE: Scaling-up Differentially Private Causal Graph Discovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhattacharjee%2C+P">Payel Bhattacharjee</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.19060v1-abstract-short" style="display: inline;"> Causal Graph Discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents joint distribution of features of a dataset. CGD-algorithms are broadly classified into two categories: (i) Constraint-based algorithms (outcome depends on conditional independence (CI) tests), (ii) Score-based algorithms (outcome depends on optimized score-function). Since, sensi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19060v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19060v1-abstract-full" style="display: none;"> Causal Graph Discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents joint distribution of features of a dataset. CGD-algorithms are broadly classified into two categories: (i) Constraint-based algorithms (outcome depends on conditional independence (CI) tests), (ii) Score-based algorithms (outcome depends on optimized score-function). Since, sensitive features of observational data is prone to privacy-leakage, Differential Privacy (DP) has been adopted to ensure user privacy in CGD. Adding same amount of noise in this sequential-natured estimation process affects the predictive performance of the algorithms. As initial CI tests in constraint-based algorithms and later iterations of the optimization process of score-based algorithms are crucial, they need to be more accurate, less noisy. Based on this key observation, we present CURATE (CaUsal gRaph AdapTivE privacy), a DP-CGD framework with adaptive privacy budgeting. In contrast to existing DP-CGD algorithms with uniform privacy budgeting across all iterations, CURATE allows adaptive privacy budgeting by minimizing error probability (for constraint-based), maximizing iterations of the optimization problem (for score-based) while keeping the cumulative leakage bounded. To validate our framework, we present a comprehensive set of experiments on several datasets and show that CURATE achieves higher utility compared to existing DP-CGD algorithms with less privacy-leakage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19060v1-abstract-full').style.display = 'none'; document.getElementById('2409.19060v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.02811">arXiv:2407.02811</a> <span> [<a href="https://arxiv.org/pdf/2407.02811">pdf</a>, <a href="https://arxiv.org/format/2407.02811">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhong%2C+M">Meiyu Zhong</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.02811v1-abstract-short" style="display: inline;"> Certifiable robustness gives the guarantee that small perturbations around an input to a classifier will not change the prediction. There are two approaches to provide certifiable robustness to adversarial examples: a) explicitly training classifiers with small Lipschitz constants, and b) Randomized smoothing, which adds random noise to the input to create a smooth classifier. We propose \textit{S… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.02811v1-abstract-full').style.display = 'inline'; document.getElementById('2407.02811v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.02811v1-abstract-full" style="display: none;"> Certifiable robustness gives the guarantee that small perturbations around an input to a classifier will not change the prediction. There are two approaches to provide certifiable robustness to adversarial examples: a) explicitly training classifiers with small Lipschitz constants, and b) Randomized smoothing, which adds random noise to the input to create a smooth classifier. We propose \textit{SPLITZ}, a practical and novel approach which leverages the synergistic benefits of both the above ideas into a single framework. Our main idea is to \textit{split} a classifier into two halves, constrain the Lipschitz constant of the first half, and smooth the second half via randomization. Motivation for \textit{SPLITZ} comes from the observation that many standard deep networks exhibit heterogeneity in Lipschitz constants across layers. \textit{SPLITZ} can exploit this heterogeneity while inheriting the scalability of randomized smoothing. We present a principled approach to train \textit{SPLITZ} and provide theoretical analysis to derive certified robustness guarantees during inference. We present a comprehensive comparison of robustness-accuracy tradeoffs and show that \textit{SPLITZ} consistently improves upon existing state-of-the-art approaches on MNIST and CIFAR-10 datasets. For instance, with $\ell_2$ norm perturbation budget of \textbf{$蔚=1$}, \textit{SPLITZ} achieves $\textbf{43.2\%}$ top-1 test accuracy on CIFAR-10 dataset compared to state-of-art top-1 test accuracy $\textbf{39.8\%} <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.02811v1-abstract-full').style.display = 'none'; document.getElementById('2407.02811v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.11195">arXiv:2405.11195</a> <span> [<a href="https://arxiv.org/pdf/2405.11195">pdf</a>, <a href="https://arxiv.org/format/2405.11195">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Trustworthy Actionable Perturbations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Friedbaum%2C+J">Jesse Friedbaum</a>, <a href="/search/cs?searchtype=author&query=Adiga%2C+S">Sudarshan Adiga</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.11195v1-abstract-short" style="display: inline;"> Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and ``foo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11195v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11195v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11195v1-abstract-full" style="display: none;"> Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and ``fool'' the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11195v1-abstract-full').style.display = 'none'; document.getElementById('2405.11195v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the 41st International Conference on Machine Learning (ICML) 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.07393">arXiv:2405.07393</a> <span> [<a href="https://arxiv.org/pdf/2405.07393">pdf</a>, <a href="https://arxiv.org/format/2405.07393">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhong%2C+M">Meiyu Zhong</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.07393v1-abstract-short" style="display: inline;"> With the growing adoption of machine learning (ML) systems in areas like law enforcement, criminal justice, finance, hiring, and admissions, it is increasingly critical to guarantee the fairness of decisions assisted by ML. In this paper, we study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy (that holds for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07393v1-abstract-full').style.display = 'inline'; document.getElementById('2405.07393v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.07393v1-abstract-full" style="display: none;"> With the growing adoption of machine learning (ML) systems in areas like law enforcement, criminal justice, finance, hiring, and admissions, it is increasingly critical to guarantee the fairness of decisions assisted by ML. In this paper, we study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy (that holds for any classifier), as a function of the fairness budget. In addition, our bounds also exhibit dependence on the underlying statistics of the data, labels and the sensitive group attributes. We validate our theoretical upper bounds through empirical analysis on three real-world datasets: COMPAS, Adult, and Law School. Specifically, we compare our upper bound to the tradeoffs that are achieved by various existing fair classifiers in the literature. Our results show that achieving high accuracy subject to a low-bias could be fundamentally limited based on the statistical disparity across the groups. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07393v1-abstract-full').style.display = 'none'; document.getElementById('2405.07393v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.14754">arXiv:2404.14754</a> <span> [<a href="https://arxiv.org/pdf/2404.14754">pdf</a>, <a href="https://arxiv.org/format/2404.14754">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3649476.3658738">10.1145/3649476.3658738 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liao%2C+Y">Yuchao Liao</a>, <a href="/search/cs?searchtype=author&query=Adegbija%2C+T">Tosiron Adegbija</a>, <a href="/search/cs?searchtype=author&query=Lysecky%2C+R">Roman Lysecky</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.14754v1-abstract-short" style="display: inline;"> High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficient for complex, multi-component system-l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14754v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14754v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14754v1-abstract-full" style="display: none;"> High-Level Synthesis (HLS) Design Space Exploration (DSE) is a widely accepted approach for efficiently exploring Pareto-optimal and optimal hardware solutions during the HLS process. Several HLS benchmarks and datasets are available for the research community to evaluate their methodologies. Unfortunately, these resources are limited and may not be sufficient for complex, multi-component system-level explorations. Generating new data using existing HLS benchmarks can be cumbersome, given the expertise and time required to effectively generate data for different HLS designs and directives. As a result, synthetic data has been used in prior work to evaluate system-level HLS DSE. However, the fidelity of the synthetic data to real data is often unclear, leading to uncertainty about the quality of system-level HLS DSE. This paper proposes a novel approach, called Vaegan, that employs generative machine learning to generate synthetic data that is robust enough to support complex system-level HLS DSE experiments that would be unattainable with only the currently available data. We explore and adapt a Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) for this task and evaluate our approach using state-of-the-art datasets and metrics. We compare our approach to prior works and show that Vaegan effectively generates synthetic HLS data that closely mirrors the ground truth's distribution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14754v1-abstract-full').style.display = 'none'; document.getElementById('2404.14754v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at Great Lakes Symposium on VLSI 2024 (GLSVLSI 24)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.14586">arXiv:2404.14586</a> <span> [<a href="https://arxiv.org/pdf/2404.14586">pdf</a>, <a href="https://arxiv.org/format/2404.14586">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Teku%2C+N">Noel Teku</a>, <a href="/search/cs?searchtype=author&query=Adiga%2C+S">Sudarshan Adiga</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.14586v1-abstract-short" style="display: inline;"> In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive applications, transmission of these decisions in a reliable and timely manner is of significant importance. To this end, we study the scenario where a probability vector (representing the decisions of a classifier) at th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14586v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14586v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14586v1-abstract-full" style="display: none;"> In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive applications, transmission of these decisions in a reliable and timely manner is of significant importance. To this end, we study the scenario where a probability vector (representing the decisions of a classifier) at the transmitter, needs to be transmitted over a noisy channel. Assuming that the distortion between the original probability vector and the reconstructed one at the receiver is measured via f-divergence, we study the trade-off between transmission latency and the distortion. We completely analyze this trade-off using uniform, lattice, and sparse lattice-based quantization techniques to encode the probability vector by first characterizing bit budgets for each technique given a requirement on the allowed source distortion. These bounds are then combined with results from finite-blocklength literature to provide a framework for analyzing the effects of both quantization distortion and distortion due to decoding error probability (i.e., channel effects) on the incurred transmission latency. Our results show that there is an interesting interplay between source distortion (i.e., distortion for the probability vector measured via f-divergence) and the subsequent channel encoding/decoding parameters; and indicate that a joint design of these parameters is crucial to navigate the latency-distortion tradeoff. We study the impact of changing different parameters (e.g. number of classes, SNR, source distortion) on the latency-distortion tradeoff and perform experiments on AWGN and fading channels. Our results indicate that sparse lattice-based quantization is the most effective at minimizing latency across various regimes and for sparse, high-dimensional probability vectors (i.e., high number of classes). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14586v1-abstract-full').style.display = 'none'; document.getElementById('2404.14586v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE Transactions on Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.14388">arXiv:2307.14388</a> <span> [<a href="https://arxiv.org/pdf/2307.14388">pdf</a>, <a href="https://arxiv.org/format/2307.14388">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Online Context-aware Data Release with Sequence Information Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+B">Bo Jiang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.14388v1-abstract-short" style="display: inline;"> Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlations prevalent within the data stream. Existing approaches either fall short in effectively leveraging these correlations, leading to a suboptimal utility-privacy tradeoff, or they involve complex mechanism designs that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14388v1-abstract-full').style.display = 'inline'; document.getElementById('2307.14388v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.14388v1-abstract-full" style="display: none;"> Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlations prevalent within the data stream. Existing approaches either fall short in effectively leveraging these correlations, leading to a suboptimal utility-privacy tradeoff, or they involve complex mechanism designs that increase the computation complexity with respect to the sequence length. In this paper, we introduce Sequence Information Privacy (SIP), a new privacy notion designed to guarantee privacy for an entire data stream, taking into account the intrinsic data correlations. We show that SIP provides a similar level of privacy guarantee compared to local differential privacy (LDP), and it also enjoys a lightweight modular mechanism design. We further study two online data release models (instantaneous or batched) and propose corresponding privacy-preserving data perturbation mechanisms. We provide a numerical evaluation of how correlations influence noise addition in data streams. Lastly, we conduct experiments using real-world data to compare the utility-privacy tradeoff offered by our approaches with those from existing literature. The results reveal that our mechanisms offer utility improvements more than twice those based on LDP-based mechanisms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14388v1-abstract-full').style.display = 'none'; document.getElementById('2307.14388v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.16552">arXiv:2306.16552</a> <span> [<a href="https://arxiv.org/pdf/2306.16552">pdf</a>, <a href="https://arxiv.org/format/2306.16552">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Learning Fair Classifiers via Min-Max F-divergence Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhong%2C+M">Meiyu Zhong</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.16552v1-abstract-short" style="display: inline;"> As machine learning (ML) based systems are adopted in domains such as law enforcement, criminal justice, finance, hiring and admissions, ensuring the fairness of ML aided decision-making is becoming increasingly important. In this paper, we focus on the problem of fair classification, and introduce a novel min-max F-divergence regularization framework for learning fair classification models while… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16552v1-abstract-full').style.display = 'inline'; document.getElementById('2306.16552v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.16552v1-abstract-full" style="display: none;"> As machine learning (ML) based systems are adopted in domains such as law enforcement, criminal justice, finance, hiring and admissions, ensuring the fairness of ML aided decision-making is becoming increasingly important. In this paper, we focus on the problem of fair classification, and introduce a novel min-max F-divergence regularization framework for learning fair classification models while preserving high accuracy. Our framework consists of two trainable networks, namely, a classifier network and a bias/fairness estimator network, where the fairness is measured using the statistical notion of F-divergence. We show that F-divergence measures possess convexity and differentiability properties, and their variational representation make them widely applicable in practical gradient based training methods. The proposed framework can be readily adapted to multiple sensitive attributes and for high dimensional datasets. We study the F-divergence based training paradigm for two types of group fairness constraints, namely, demographic parity and equalized odds. We present a comprehensive set of experiments for several real-world data sets arising in multiple domains (including COMPAS, Law Admissions, Adult Income, and CelebA datasets). To quantify the fairness-accuracy tradeoff, we introduce the notion of fairness-accuracy receiver operating characteristic (FA-ROC) and a corresponding \textit{low-bias} FA-ROC, which we argue is an appropriate measure to evaluate different classifiers. In comparison to several existing approaches for learning fair classifiers (including pre-processing, post-processing and other regularization methods), we show that the proposed F-divergence based framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.16552v1-abstract-full').style.display = 'none'; document.getElementById('2306.16552v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10540">arXiv:2305.10540</a> <span> [<a href="https://arxiv.org/pdf/2305.10540">pdf</a>, <a href="https://arxiv.org/format/2305.10540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TIT.2024.3361388">10.1109/TIT.2024.3361388 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Generalization Bounds for Neural Belief Propagation Decoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Adiga%2C+S">Sudarshan Adiga</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+X">Xin Xiao</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Vasic%2C+B">Bane Vasic</a>, <a href="/search/cs?searchtype=author&query=Bose%2C+T">Tamal Bose</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.10540v2-abstract-short" style="display: inline;"> Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to improve upon classical decoding algorithms… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10540v2-abstract-full').style.display = 'inline'; document.getElementById('2305.10540v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10540v2-abstract-full" style="display: none;"> Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to improve upon classical decoding algorithms. In this paper, we investigate the generalization capabilities of NBP decoders. Specifically, the generalization gap of a decoder is the difference between empirical and expected bit-error-rate(s). We present new theoretical results which bound this gap and show the dependence on the decoder complexity, in terms of code parameters (blocklength, message length, variable/check node degrees), decoding iterations, and the training dataset size. Results are presented for both regular and irregular parity-check matrices. To the best of our knowledge, this is the first set of theoretical results on generalization performance of neural network based decoders. We present experimental results to show the dependence of generalization gap on the training dataset size, and decoding iterations for different codes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10540v2-abstract-full').style.display = 'none'; document.getElementById('2305.10540v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in IEEE Transactions on Information Theory (2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.04918">arXiv:2211.04918</a> <span> [<a href="https://arxiv.org/pdf/2211.04918">pdf</a>, <a href="https://arxiv.org/format/2211.04918">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Methodology">stat.ME</span> </div> </div> <p class="title is-5 mathjax"> Detection of Sparse Anomalies in High-Dimensional Network Telescope Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kartsioukas%2C+R">Rafail Kartsioukas</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Z">Zheng Gao</a>, <a href="/search/cs?searchtype=author&query=Mirkovic%2C+J">Jelena Mirkovic</a>, <a href="/search/cs?searchtype=author&query=Kallitsis%2C+M">Michalis Kallitsis</a>, <a href="/search/cs?searchtype=author&query=Stoev%2C+S">Stilian Stoev</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.04918v2-abstract-short" style="display: inline;"> Network operators and system administrators are increasingly overwhelmed with incessant cyber-security threats ranging from malicious network reconnaissance to attacks such as distributed denial of service and data breaches. A large number of these attacks could be prevented if the network operators were better equipped with threat intelligence information that would allow them to block or throttl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04918v2-abstract-full').style.display = 'inline'; document.getElementById('2211.04918v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.04918v2-abstract-full" style="display: none;"> Network operators and system administrators are increasingly overwhelmed with incessant cyber-security threats ranging from malicious network reconnaissance to attacks such as distributed denial of service and data breaches. A large number of these attacks could be prevented if the network operators were better equipped with threat intelligence information that would allow them to block or throttle nefarious scanning activities. Network telescopes or "darknets" offer a unique window into observing Internet-wide scanners and other malicious entities, and they could offer early warning signals to operators that would be critical for infrastructure protection and/or attack mitigation. A network telescope consists of unused or "dark" IP spaces that serve no users, and solely passively observes any Internet traffic destined to the "telescope sensor" in an attempt to record ubiquitous network scanners, malware that forage for vulnerable devices, and other dubious activities. Hence, monitoring network telescopes for timely detection of coordinated and heavy scanning activities is an important, albeit challenging, task. The challenges mainly arise due to the non-stationarity and the dynamic nature of Internet traffic and, more importantly, the fact that one needs to monitor high-dimensional signals (e.g., all TCP/UDP ports) to search for "sparse" anomalies. We propose statistical methods to address both challenges in an efficient and "online" manner; our work is validated both with synthetic data as well as real-world data from a large network telescope. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04918v2-abstract-full').style.display = 'none'; document.getElementById('2211.04918v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.02356">arXiv:2211.02356</a> <span> [<a href="https://arxiv.org/pdf/2211.02356">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Did your child get disturbed by an inappropriate advertisement on YouTube? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jeffrey Liu</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a>, <a href="/search/cs?searchtype=author&query=Durairaj%2C+U">Uma Durairaj</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+J">Jiani Guo</a>, <a href="/search/cs?searchtype=author&query=Zahabizadeh%2C+S">Spencer Zahabizadeh</a>, <a href="/search/cs?searchtype=author&query=Ilango%2C+S">Sanjana Ilango</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+J">Jeremy Tang</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+N">Neelesh Gupta</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Z">Zoe Zhou</a>, <a href="/search/cs?searchtype=author&query=Mirkovic%2C+J">Jelena Mirkovic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.02356v1-abstract-short" style="display: inline;"> YouTube is a popular video platform for sharing creative content and ideas, targeting different demographics. Adults, older children, and young children are all avid viewers of YouTube videos. Meanwhile, countless young-kid-oriented channels have produced numerous instructional and age appropriate videos for young children. However, inappropriate content for young children, such as violent or sexu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02356v1-abstract-full').style.display = 'inline'; document.getElementById('2211.02356v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.02356v1-abstract-full" style="display: none;"> YouTube is a popular video platform for sharing creative content and ideas, targeting different demographics. Adults, older children, and young children are all avid viewers of YouTube videos. Meanwhile, countless young-kid-oriented channels have produced numerous instructional and age appropriate videos for young children. However, inappropriate content for young children, such as violent or sexually suggestive content, still exists. And children lack the ability to decide whether a video is appropriate for them or not, which then causes a huge risk to children's mental health. Prior works have focused on identifying YouTube videos that are inappropriate for children. However, these works ignore that not only the actual video content influences children, but also the advertisements that are shown with those videos. In this paper, we quantify the influence of inappropriate advertisements on YouTube videos that are appropriate for young children to watch. We analyze the advertising patterns of 24.6 K diverse YouTube videos appropriate for young children. We find that 9.9% of the 4.6 K unique advertisements shown on these 24.6 K videos contain inappropriate content for young children. Moreover, we observe that 26.9% of all the 24.6 K appropriate videos include at least one ad that is inappropriate for young children. Additionally, we publicly release our datasets and provide recommendations about how to address this issue. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02356v1-abstract-full').style.display = 'none'; document.getElementById('2211.02356v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In Proceedings of KDD Undergraduate Consortium (KDD-UC 2022)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.01806">arXiv:2202.01806</a> <span> [<a href="https://arxiv.org/pdf/2202.01806">pdf</a>, <a href="https://arxiv.org/ps/2202.01806">ps</a>, <a href="https://arxiv.org/format/2202.01806">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TIFS.2023.3288812">10.1109/TIFS.2023.3288812 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Answering Count Queries for Genomic Data with Perfect Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+B">Bo Jiang</a>, <a href="/search/cs?searchtype=author&query=Seif%2C+M">Mohamed Seif</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.01806v2-abstract-short" style="display: inline;"> In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect aggregate (population-wide) information from biomedical Databases (DBs) for analysis, such as Genome-wide association studies. Our goal is to design mechanisms for answering count queries of the following form: \textit{… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01806v2-abstract-full').style.display = 'inline'; document.getElementById('2202.01806v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.01806v2-abstract-full" style="display: none;"> In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect aggregate (population-wide) information from biomedical Databases (DBs) for analysis, such as Genome-wide association studies. Our goal is to design mechanisms for answering count queries of the following form: \textit{How many users in the database have a specific set of genotypes at certain locations in their genome?} At the same time, we aim to achieve perfect privacy (zero information leakage) of the sensitive genotypes at a pre-specified set of secret locations. The sensitive genotypes could indicate rare diseases and/or other health traits one may want to keep private. We present both local and central count-query mechanisms for the above problem that achieves perfect information-theoretic privacy for sensitive genotypes while minimizing the expected absolute error (or per-user error probability, depending on the setting) of the query answer. We also derived a lower bound of the per-user probability of error for an arbitrary query-answering mechanism that satisfies perfect privacy. We show that our mechanisms achieve error close to the lower bound, and match the lower bound for some special cases. We numerically show that the performance of each mechanism depends on the data prior distribution, the intersection between the queried and sensitive genotypes, and the strength of the correlation in the genomic data sequence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.01806v2-abstract-full').style.display = 'none'; document.getElementById('2202.01806v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">B. Jiang, M. Seif, R. Tandon and M. Li, "Answering Count Queries for Genomic Data With Perfect Privacy," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 3862-3875, 2023, doi: 10.1109/TIFS.2023.3288812</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> TIFS.2023.3288812 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.00636">arXiv:2202.00636</a> <span> [<a href="https://arxiv.org/pdf/2202.00636">pdf</a>, <a href="https://arxiv.org/format/2202.00636">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Differentially Private Community Detection for Stochastic Block Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seif%2C+M">Mohamed Seif</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+D">Dung Nguyen</a>, <a href="/search/cs?searchtype=author&query=Vullikanti%2C+A">Anil Vullikanti</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.00636v2-abstract-short" style="display: inline;"> The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by adjacency matrix of a graph). There has been significant recent progress on understanding the fundamental limits of community detection when the graph is generated from a stochastic block model (SBM). Specifically, sharp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00636v2-abstract-full').style.display = 'inline'; document.getElementById('2202.00636v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.00636v2-abstract-full" style="display: none;"> The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by adjacency matrix of a graph). There has been significant recent progress on understanding the fundamental limits of community detection when the graph is generated from a stochastic block model (SBM). Specifically, sharp information theoretic limits and efficient algorithms have been obtained for SBMs as a function of $p$ and $q$, which represent the intra-community and inter-community connection probabilities. In this paper, we study the community detection problem while preserving the privacy of the individual connections (edges) between the vertices. Focusing on the notion of $(蔚, 未)$-edge differential privacy (DP), we seek to understand the fundamental tradeoffs between $(p, q)$, DP budget $(蔚, 未)$, and computational efficiency for exact recovery of the community labels. To this end, we present and analyze the associated information-theoretic tradeoffs for three broad classes of differentially private community recovery mechanisms: a) stability based mechanism; b) sampling based mechanisms; and c) graph perturbation mechanisms. Our main findings are that stability and sampling based mechanisms lead to a superior tradeoff between $(p,q)$ and the privacy budget $(蔚, 未)$; however this comes at the expense of higher computational complexity. On the other hand, albeit low complexity, graph perturbation mechanisms require the privacy budget $蔚$ to scale as $惟(\log(n))$ for exact recovery. To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00636v2-abstract-full').style.display = 'none'; document.getElementById('2202.00636v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ICML 2022. https://proceedings.mlr.press/v162/mohamed22a.html</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.11678">arXiv:2201.11678</a> <span> [<a href="https://arxiv.org/pdf/2201.11678">pdf</a>, <a href="https://arxiv.org/format/2201.11678">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Change Detection using DRE-CUSUM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Adiga%2C+S">Sudarshan Adiga</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.11678v1-abstract-short" style="display: inline;"> This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11678v1-abstract-full').style.display = 'inline'; document.getElementById('2201.11678v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.11678v1-abstract-full" style="display: none;"> This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a neural network) before and after the split point. The DRE-CUSUM change detection statistic is then derived from the cumulative sum (CUSUM) of the logarithm of the estimated density ratio. We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point. While there have been prior works on using density ratio based methods for change detection, to the best of our knowledge, this is the first unsupervised change detection approach with a theoretical justification and accuracy guarantees. The simplicity of the proposed framework makes it readily applicable in various practical settings (including high-dimensional time-series data); we also discuss generalizations for online change detection. We experimentally show the superiority of DRE-CUSUM using both synthetic and real-world datasets over existing state-of-the-art unsupervised algorithms (such as Bayesian online change detection, its variants as well as several other heuristic methods). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11678v1-abstract-full').style.display = 'none'; document.getElementById('2201.11678v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.04649">arXiv:2109.04649</a> <span> [<a href="https://arxiv.org/pdf/2109.04649">pdf</a>, <a href="https://arxiv.org/format/2109.04649">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Utilizing Shannon's Entropy to Create Privacy Aware Architectures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Palia%2C+A">Abhinav Palia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a>, <a href="/search/cs?searchtype=author&query=Mathis%2C+C">Carl Mathis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.04649v2-abstract-short" style="display: inline;"> Privacy is an individual choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems. The goal of a privacy-aware design is to protect information in a way that does not increase an adversary's exi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.04649v2-abstract-full').style.display = 'inline'; document.getElementById('2109.04649v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.04649v2-abstract-full" style="display: none;"> Privacy is an individual choice to determine which personal details can be collected, used and shared. Individual consent and transparency are the core tenets for earning customers trust and this motivates the organizations to adopt privacy enhancing practices while creating the systems. The goal of a privacy-aware design is to protect information in a way that does not increase an adversary's existing knowledge about an individual beyond what is permissible. This becomes critical when these data elements can be linked with the wealth of auxiliary information available outside the system to identify an individual. Privacy regulations around the world provide directives to protect individual privacy but are generally complex and vague, making their translation into actionable and technical privacy-friendly architectures challenging. In this paper, we utilize Shannon's Entropy to create an objective metric that can help simplify the state-of-the-art Privacy Design Strategies proposed in the literature and aid our key technical design decisions to create privacy aware architectures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.04649v2-abstract-full').style.display = 'none'; document.getElementById('2109.04649v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.00026">arXiv:2108.00026</a> <span> [<a href="https://arxiv.org/pdf/2108.00026">pdf</a>, <a href="https://arxiv.org/format/2108.00026">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Private Retrieval, Computing and Learning: Recent Progress and Future Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ulukus%2C+S">Sennur Ulukus</a>, <a href="/search/cs?searchtype=author&query=Avestimehr%2C+S">Salman Avestimehr</a>, <a href="/search/cs?searchtype=author&query=Gastpar%2C+M">Michael Gastpar</a>, <a href="/search/cs?searchtype=author&query=Jafar%2C+S">Syed Jafar</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Tian%2C+C">Chao Tian</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.00026v1-abstract-short" style="display: inline;"> Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distribu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00026v1-abstract-full').style.display = 'inline'; document.getElementById('2108.00026v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.00026v1-abstract-full" style="display: none;"> Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distributed computing), and how to learn/train machine learning models from private data spread across multiple users (privacy in distributed (federated) learning). The article motivates each privacy setting, describes the problem formulation, summarizes breakthrough results in the history of each problem, and gives recent results and discusses some of the major ideas that emerged in each field. In addition, the cross-cutting techniques and interconnections between the three topics are discussed along with a set of open problems and challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00026v1-abstract-full').style.display = 'none'; document.getElementById('2108.00026v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.04118">arXiv:2105.04118</a> <span> [<a href="https://arxiv.org/pdf/2105.04118">pdf</a>, <a href="https://arxiv.org/format/2105.04118">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> FAID Diversity via Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xiao%2C+X">Xin Xiao</a>, <a href="/search/cs?searchtype=author&query=Raveendran%2C+N">Nithin Raveendran</a>, <a href="/search/cs?searchtype=author&query=Vasic%2C+B">Bane Vasic</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+S">Shu Lin</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2105.04118v1-abstract-short" style="display: inline;"> Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individual decoder. In this paper, we propose a new approach to design the decoder diversity of finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over the binary symmetric channel (BSC), for th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.04118v1-abstract-full').style.display = 'inline'; document.getElementById('2105.04118v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.04118v1-abstract-full" style="display: none;"> Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individual decoder. In this paper, we propose a new approach to design the decoder diversity of finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over the binary symmetric channel (BSC), for the purpose of lowering the error floor while guaranteeing the waterfall performance. The proposed decoder diversity is achieved by training a recurrent quantized neural network (RQNN) to learn/design FAIDs. We demonstrated for the first time that a machine-learned decoder can surpass in performance a man-made decoder of the same complexity. As RQNNs can model a broad class of FAIDs, they are capable of learning an arbitrary FAID. To provide sufficient knowledge of the error floor to the RQNN, the training sets are constructed by sampling from the set of most problematic error patterns - trapping sets. In contrast to the existing methods that use the cross-entropy function as the loss function, we introduce a frame-error-rate (FER) based loss function to train the RQNN with the objective of correcting specific error patterns rather than reducing the bit error rate (BER). The examples and simulation results show that the RQNN-aided decoder diversity increases the error correction capability of LDPC codes and lowers the error floor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.04118v1-abstract-full').style.display = 'none'; document.getElementById('2105.04118v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 3 figures, 3 tables. A shorter version is submitted to the International Symposium on Topics in Coding, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.01953">arXiv:2103.01953</a> <span> [<a href="https://arxiv.org/pdf/2103.01953">pdf</a>, <a href="https://arxiv.org/format/2103.01953">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seif%2C+M">Mohamed Seif</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.01953v1-abstract-short" style="display: inline;"> In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarante… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.01953v1-abstract-full').style.display = 'inline'; document.getElementById('2103.01953v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.01953v1-abstract-full" style="display: none;"> In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/K^{1/2})$, where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by join incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal{O}(1/K^{3/4})$. In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are $(a)$ known, or $(b)$ unknown at the parameter server. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.01953v1-abstract-full').style.display = 'none'; document.getElementById('2103.01953v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.14503">arXiv:2010.14503</a> <span> [<a href="https://arxiv.org/pdf/2010.14503">pdf</a>, <a href="https://arxiv.org/format/2010.14503">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Topological Interference Management with Confidential Messages </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mutangana%2C+J+d+D">Jean de Dieu Mutangana</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.14503v2-abstract-short" style="display: inline;"> The topological interference management (TIM) problem refers to the study of the K-user partially connected interference networks with no channel state information at the transmitters (CSIT), except for the knowledge of network topology. In this paper, we study the TIM problem with confidential messages (TIM-CM), where message confidentiality must be satisfied in addition to reliability constraint… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.14503v2-abstract-full').style.display = 'inline'; document.getElementById('2010.14503v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.14503v2-abstract-full" style="display: none;"> The topological interference management (TIM) problem refers to the study of the K-user partially connected interference networks with no channel state information at the transmitters (CSIT), except for the knowledge of network topology. In this paper, we study the TIM problem with confidential messages (TIM-CM), where message confidentiality must be satisfied in addition to reliability constraints. In particular, each transmitted message must be decodable at its intended receiver and remain confidential at the remaining (K-1) receivers. Our main contribution is to present a comprehensive set of results for the TIM-CM problem by studying the symmetric secure degrees of freedom (SDoF). To this end, we first characterize necessary and sufficient conditions for feasibility of positive symmetric SDoF for any arbitrary topology. We next present two achievable schemes for the TIM-CM problem: For the first scheme, we use the concept of secure partition and, for the second one, we use the concept of secure independent sets. We also present outer bounds on symmetric SDoF for any arbitrary network topology. Using these bounds, we characterize the optimal symmetric SDoF of all K=2-user and K=3-user network topologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.14503v2-abstract-full').style.display = 'none'; document.getElementById('2010.14503v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted and published in IEEE Transactions on Information Theory</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.06785">arXiv:2008.06785</a> <span> [<a href="https://arxiv.org/pdf/2008.06785">pdf</a>, <a href="https://arxiv.org/format/2008.06785">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adversarial Filters for Secure Modulation Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Berian%2C+A">Alex Berian</a>, <a href="/search/cs?searchtype=author&query=Staab%2C+K">Kory Staab</a>, <a href="/search/cs?searchtype=author&query=Teku%2C+N">Noel Teku</a>, <a href="/search/cs?searchtype=author&query=Ditzler%2C+G">Gregory Ditzler</a>, <a href="/search/cs?searchtype=author&query=Bose%2C+T">Tamal Bose</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.06785v1-abstract-short" style="display: inline;"> Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation performed on the received signal and is critical for reliable decoding. This paper considers the problem of secure modulation classification, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.06785v1-abstract-full').style.display = 'inline'; document.getElementById('2008.06785v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.06785v1-abstract-full" style="display: none;"> Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation performed on the received signal and is critical for reliable decoding. This paper considers the problem of secure modulation classification, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve). The contribution of this work is to design novel adversarial learning techniques for secure MC. In particular, we present adversarial filtering based algorithms for secure MC, in which Alice uses a carefully designed adversarial filter to mask the transmitted signal, that can maximize MC accuracy at Bob while minimizing MC accuracy at Eve. We present two filtering based algorithms, namely gradient ascent filter (GAF), and a fast gradient filter method (FGFM), with varying levels of complexity. Our proposed adversarial filtering based approaches significantly outperform additive adversarial perturbations (used in the traditional ML community and other prior works on secure MC) and also have several other desirable properties. In particular, GAF and FGFM algorithms are a) computational efficient (allow fast decoding at Bob), b) power-efficient (do not require excessive transmit power at Alice); and c) SNR efficient (i.e., perform well even at low SNR values at Bob). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.06785v1-abstract-full').style.display = 'none'; document.getElementById('2008.06785v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This is a placeholder to show that we are the first to do something like this. We intend to submit this work to a journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.01345">arXiv:2008.01345</a> <span> [<a href="https://arxiv.org/pdf/2008.01345">pdf</a>, <a href="https://arxiv.org/format/2008.01345">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> A Survey of Distributed Denial of Service Attacks and Defenses </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.01345v1-abstract-short" style="display: inline;"> A distributed denial-of-service (DDoS) attack is an attack wherein multiple compromised computer systems flood the bandwidth and/or resources of a target, such as a server, website or other network resource, and cause a denial of service for users of the targeted resource. The flood of incoming messages, connection requests or malformed packets to the target system forces it to slow down or even c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.01345v1-abstract-full').style.display = 'inline'; document.getElementById('2008.01345v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.01345v1-abstract-full" style="display: none;"> A distributed denial-of-service (DDoS) attack is an attack wherein multiple compromised computer systems flood the bandwidth and/or resources of a target, such as a server, website or other network resource, and cause a denial of service for users of the targeted resource. The flood of incoming messages, connection requests or malformed packets to the target system forces it to slow down or even crash and shut down, thereby denying service to legitimate users or systems. This paper presents a literature review of DDoS attacks and the common defense mechanisms available. It also presents a literature review of the defenses for low-rate DDoS attacks that have not been handled effectively hitherto. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.01345v1-abstract-full').style.display = 'none'; document.getElementById('2008.01345v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.03048">arXiv:2006.03048</a> <span> [<a href="https://arxiv.org/pdf/2006.03048">pdf</a>, <a href="https://arxiv.org/format/2006.03048">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Asymmetric Leaky Private Information Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samy%2C+I">Islam Samy</a>, <a href="/search/cs?searchtype=author&query=Attia%2C+M+A">Mohamed A. Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Lazos%2C+L">Loukas Lazos</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.03048v1-abstract-short" style="display: inline;"> Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of $K$ messages from $N$ non-colluding replicated databases without learning anything about the remaining $K-1$ messages. However, the goal of perfect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03048v1-abstract-full').style.display = 'inline'; document.getElementById('2006.03048v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.03048v1-abstract-full" style="display: none;"> Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of $K$ messages from $N$ non-colluding replicated databases without learning anything about the remaining $K-1$ messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant $蔚$. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant $未$. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary $蔚$ and $未$. We show that the optimal download cost of AL-PIR is upper-bounded as $D^{*}(蔚,未)\leq 1+\frac{1}{N-1}-\frac{未e^蔚}{N^{K-1}-1}$. Second, we obtain an information-theoretic lower bound on the download cost as $D^{*}(蔚,未)\geq 1+\frac{1}{Ne^蔚-1}-\frac未{(Ne^蔚)^{K-1}-1}$. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when $蔚=0$, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of $\frac{N-e^{-蔚}}{N-1}$ for any $(蔚,未)$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03048v1-abstract-full').style.display = 'none'; document.getElementById('2006.03048v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.05151">arXiv:2002.05151</a> <span> [<a href="https://arxiv.org/pdf/2002.05151">pdf</a>, <a href="https://arxiv.org/format/2002.05151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Wireless Federated Learning with Local Differential Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seif%2C+M">Mohamed Seif</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.05151v1-abstract-short" style="display: inline;"> In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints. We show that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users. We propose a p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05151v1-abstract-full').style.display = 'inline'; document.getElementById('2002.05151v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.05151v1-abstract-full" style="display: none;"> In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints. We show that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users. We propose a private wireless gradient aggregation scheme, which shows that when aggregating gradients from $K$ users, the privacy leakage per user scales as $\mathcal{O}\big(\frac{1}{\sqrt{K}} \big)$ compared to orthogonal transmission in which the privacy leakage scales as a constant. We also present analysis for the convergence rate of the proposed private FL aggregation algorithm and study the tradeoffs between wireless resources, convergence, and privacy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05151v1-abstract-full').style.display = 'none'; document.getElementById('2002.05151v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.08737">arXiv:2001.08737</a> <span> [<a href="https://arxiv.org/pdf/2001.08737">pdf</a>, <a href="https://arxiv.org/format/2001.08737">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Communication Efficient Federated Learning over Multiple Access Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.08737v1-abstract-short" style="display: inline;"> In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.08737v1-abstract-full').style.display = 'inline'; document.getElementById('2001.08737v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.08737v1-abstract-full" style="display: none;"> In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL learning over a MAC. In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (such as rate or bandwidth) to different users based on a) the informativeness of the gradients at each user, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.08737v1-abstract-full').style.display = 'none'; document.getElementById('2001.08737v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.05998">arXiv:2001.05998</a> <span> [<a href="https://arxiv.org/pdf/2001.05998">pdf</a>, <a href="https://arxiv.org/format/2001.05998">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Latent-variable Private Information Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Samy%2C+I">Islam Samy</a>, <a href="/search/cs?searchtype=author&query=Attia%2C+M+A">Mohamed A. Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Lazos%2C+L">Loukas Lazos</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.05998v2-abstract-short" style="display: inline;"> In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This so… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.05998v2-abstract-full').style.display = 'inline'; document.getElementById('2001.05998v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.05998v2-abstract-full" style="display: none;"> In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding $K$ messages, privately retrieving one message is possible if and only if the user downloads the entire database of $K$ messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes. Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of $K$ messages (indexed by $胃$) without revealing any information about the latent variable (modeled by $S$). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between $胃$ and $S$. We present a general scheme for LV-PIR as a function of the statistical relationship between $胃$ and $S$, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.05998v2-abstract-full').style.display = 'none'; document.getElementById('2001.05998v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.02385">arXiv:2001.02385</a> <span> [<a href="https://arxiv.org/pdf/2001.02385">pdf</a>, <a href="https://arxiv.org/format/2001.02385">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+B">Bo Jiang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.02385v2-abstract-short" style="display: inline;"> In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP, which can be viewed as explicit modeling of the adversary's background knowledge. It enables the design of privacy-preserving mechanisms leveraging the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02385v2-abstract-full').style.display = 'inline'; document.getElementById('2001.02385v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.02385v2-abstract-full" style="display: none;"> In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP, which can be viewed as explicit modeling of the adversary's background knowledge. It enables the design of privacy-preserving mechanisms leveraging the prior distribution, which can potentially achieve a better utility-privacy tradeoff than context-free notions such as Local Differential Privacy (LDP). We present an optimization framework to minimize the mean square error in the data aggregation while protecting the privacy of each individual user's input data or a correlated latent variable while satisfying LIP constraints. Then, we study two different types of applications: (weighted) summation and histogram estimation and derive the optimal context-aware data perturbation parameters for each case, based on randomized response type of mechanism. We further compare the utility-privacy tradeoff between LIP and LDP and theoretically explain why the incorporation of prior knowledge enlarges feasible regions of the perturbation parameters, which thereby leads to higher utility. We also extend the LIP-based privacy mechanisms to the more general case when exact prior knowledge is not available. Finally, we validate our analysis by simulations using both synthetic and real-world data. Results show that our LIP-based privacy mechanism provides better utility-privacy tradeoffs than LDP, and the advantage of LIP is even more significant when the prior distribution is more skewed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02385v2-abstract-full').style.display = 'none'; document.getElementById('2001.02385v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.09467">arXiv:1912.09467</a> <span> [<a href="https://arxiv.org/pdf/1912.09467">pdf</a>, <a href="https://arxiv.org/format/1912.09467">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Cache-Aided Content Delivery in Fog-RAN Systems with Topological Information and no CSI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Simeone%2C+O">Osvaldo Simeone</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.09467v1-abstract-short" style="display: inline;"> In this work, we consider a Fog Radio Access Network (F-RAN) system with a partially connected wireless topology and no channel state information available at the cloud and Edge Nodes (ENs). An F-RAN consists of ENs that are equipped with caches and connected to the cloud through fronthaul links. We first focus on the case where cloud connectivity is disabled, and hence the ENs have to satisfy the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.09467v1-abstract-full').style.display = 'inline'; document.getElementById('1912.09467v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.09467v1-abstract-full" style="display: none;"> In this work, we consider a Fog Radio Access Network (F-RAN) system with a partially connected wireless topology and no channel state information available at the cloud and Edge Nodes (ENs). An F-RAN consists of ENs that are equipped with caches and connected to the cloud through fronthaul links. We first focus on the case where cloud connectivity is disabled, and hence the ENs have to satisfy the user demands based only on their cache contents. For a class of partially connected regular networks, we present a delivery scheme which combines intra-file MDS coded caching at the ENs and blind interference avoidance on the wireless edge channel. This scheme requires minimum storage and leads to an achievable Normalized Delivery Time (NDT) that is within a constant multiplicative factor of the best known NDT with full storage. We next provide achievable schemes for the case where cloud connectivity is enabled, and we provide new insights on the interplay between cloud connectivity and edge caching when only topological information is available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.09467v1-abstract-full').style.display = 'none'; document.getElementById('1912.09467v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work was presented at the 2017 51st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, USA</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.10684">arXiv:1906.10684</a> <span> [<a href="https://arxiv.org/pdf/1906.10684">pdf</a>, <a href="https://arxiv.org/format/1906.10684">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> On the Upload versus Download Cost for Secure and Private Matrix Multiplication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.10684v1-abstract-short" style="display: inline;"> In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix $A$, with a matrix $B_胃$, where $胃\in\{1,\dots,M\}$. The set of candidate matrices $\{B_1,\dots,B_M\}$ are public, and available at all the $N$ servers. The goal of the user is to distributedly compute… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.10684v1-abstract-full').style.display = 'inline'; document.getElementById('1906.10684v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.10684v1-abstract-full" style="display: none;"> In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a confidential matrix $A$, with a matrix $B_胃$, where $胃\in\{1,\dots,M\}$. The set of candidate matrices $\{B_1,\dots,B_M\}$ are public, and available at all the $N$ servers. The goal of the user is to distributedly compute $AB_胃$, such that $(a)$ no information is leaked about the matrix $A$ to any server; and $(b)$ the index $胃$ is kept private from each server. Our goal is to understand the fundamental tradeoff between the upload vs download cost for this problem. Our main contribution is to show that the lower convex hull of following (upload, download) pairs: $(U,D)=(N/(K-1),(K/(K-1))(1+(K/N)+\dots+(K/N)^{M-1}))$ for $K=2,\dots,N$ is achievable. The scheme improves upon state-of-the-art existing schemes for this problem, and leverages ideas from secret sharing and coded private information retrieval. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.10684v1-abstract-full').style.display = 'none'; document.getElementById('1906.10684v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear at IEEE Information Theory Workshop (ITW) 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.06942">arXiv:1905.06942</a> <span> [<a href="https://arxiv.org/pdf/1905.06942">pdf</a>, <a href="https://arxiv.org/format/1905.06942">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Random Sampling for Distributed Coded Matrix Multiplication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.06942v1-abstract-short" style="display: inline;"> Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower computations). In many scenarios, instead of exact computation, approximate matr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06942v1-abstract-full').style.display = 'inline'; document.getElementById('1905.06942v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.06942v1-abstract-full" style="display: none;"> Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower computations). In many scenarios, instead of exact computation, approximate matrix multiplication, i.e., allowing for a tolerable error is also sufficient. Such approximate schemes make use of randomization techniques to speed up the computation process. In this paper, we initiate the study of approximate coded matrix multiplication, and investigate the joint synergies offered by randomization and coding. Specifically, we propose two coded randomized sampling schemes that use (a) codes to achieve a desired recovery threshold and (b) random sampling to obtain approximation of the matrix multiplication. Tradeoffs between the recovery threshold and approximation error obtained through random sampling are investigated for a class of coded matrix multiplication schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06942v1-abstract-full').style.display = 'none'; document.getElementById('1905.06942v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.00469">arXiv:1806.00469</a> <span> [<a href="https://arxiv.org/pdf/1806.00469">pdf</a>, <a href="https://arxiv.org/format/1806.00469">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> On the Capacity of Secure Distributed Matrix Multiplication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wei-Ting Chang</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1806.00469v1-abstract-short" style="display: inline;"> Matrix multiplication is one of the key operations in various engineering applications. Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud is desirable to speed up computation. However, security becomes an issue when these servers are untrustworthy. In this paper, we study the problem of secure distributed matrix multiplication from distributed untrustwort… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00469v1-abstract-full').style.display = 'inline'; document.getElementById('1806.00469v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.00469v1-abstract-full" style="display: none;"> Matrix multiplication is one of the key operations in various engineering applications. Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud is desirable to speed up computation. However, security becomes an issue when these servers are untrustworthy. In this paper, we study the problem of secure distributed matrix multiplication from distributed untrustworthy servers. This problem falls in the category of secure function computation and has received significant attention in the cryptography community. However, the fundamental limits of information-theoretically secure matrix multiplication remain an open problem. We focus on information-theoretically secure distributed matrix multiplication with the goal of characterizing the minimum communication overhead. The capacity of secure matrix multiplication is defined as the maximum possible ratio of the desired information and the total communication received from $N$ distributed servers. In particular, we study the following two models where we want to multiply two matrices $A\in\mathbb{F}^{m\times n}$ and $B\in\mathbb{F}^{n\times p}$: $(a)$ one-sided secure matrix multiplication with $\ell$ colluding servers, in which $B$ is a public matrix available at all servers and $A$ is a private matrix. $(b)$ fully secure matrix multiplication with $\ell$ colluding servers, in which both $A$ and $B$ are private matrices. The goal is to securely multiply $A$ and $B$ when any $\ell$ servers can collude. For model $(a)$, we characterize the capacity as $C_{\text{one-sided}}^{(\ell)}=(N-\ell)/N$ by providing a secure matrix multiplication scheme and a matching converse. For model $(b)$, we propose a novel scheme that lower bounds the capacity, i.e., $C_{\text{fully}}^{(\ell)}\geq (\lceil \sqrt{N}-\ell \rceil)^2/(\lceil \sqrt{N}-\ell \rceil+\ell)^2$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00469v1-abstract-full').style.display = 'none'; document.getElementById('1806.00469v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.04104">arXiv:1805.04104</a> <span> [<a href="https://arxiv.org/pdf/1805.04104">pdf</a>, <a href="https://arxiv.org/format/1805.04104">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> The Capacity of Private Information Retrieval from Uncoded Storage Constrained Databases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Attia%2C+M+A">Mohamed Adel Attia</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+D">Deepak Kumar</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1805.04104v2-abstract-short" style="display: inline;"> Private information retrieval (PIR) allows a user to retrieve a desired message from a set of databases without revealing the identity of the desired message. The replicated databases scenario was considered by Sun and Jafar, 2016, where $N$ databases can store the same $K$ messages completely. A PIR scheme was developed to achieve the optimal download cost given by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.04104v2-abstract-full').style.display = 'inline'; document.getElementById('1805.04104v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.04104v2-abstract-full" style="display: none;"> Private information retrieval (PIR) allows a user to retrieve a desired message from a set of databases without revealing the identity of the desired message. The replicated databases scenario was considered by Sun and Jafar, 2016, where $N$ databases can store the same $K$ messages completely. A PIR scheme was developed to achieve the optimal download cost given by $\left(1+ \frac{1}{N}+ \frac{1}{N^{2}}+ \cdots + \frac{1}{N^{K-1}}\right)$. In this work, we consider the problem of PIR from storage constrained databases. Each database has a storage capacity of $渭KL$ bits, where $L$ is the size of each message in bits, and $渭\in [1/N, 1]$ is the normalized storage. On one extreme, $渭=1$ is the replicated databases case. On the other hand, when $渭= 1/N$, then in order to retrieve a message privately, the user has to download all the messages from the databases achieving a download cost of $1/K$. We aim to characterize the optimal download cost versus storage trade-off for any storage capacity in the range $渭\in [1/N, 1]$. For any $(N,K)$, we show that the optimal trade-off between storage, $渭$, and the download cost, $D(渭)$, is given by the lower convex hull of the $N$ pairs $\left(渭= \frac{t}{N},D(渭) = \left(1+ \frac{1}{t}+ \frac{1}{t^{2}}+ \cdots + \frac{1}{t^{K-1}}\right)\right)$ for $t=1,2,\ldots, N$. To prove this result, we first present the storage constrained PIR scheme for any $(N,K)$. We next obtain a general lower bound on the download cost for PIR, which is valid for the following storage scenarios: replicated or storage constrained, coded or uncoded, and fixed or optimized. We then specialize this bound using the uncoded storage assumption to obtain lower bounds matching the achievable download cost of the storage constrained PIR scheme for any value of the available storage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.04104v2-abstract-full').style.display = 'none'; document.getElementById('1805.04104v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.07886">arXiv:1804.07886</a> <span> [<a href="https://arxiv.org/pdf/1804.07886">pdf</a>, <a href="https://arxiv.org/format/1804.07886">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ASONAM.2018.8508382">10.1109/ASONAM.2018.8508382 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Social Bots for Online Public Health Interventions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Deb%2C+A">Ashok Deb</a>, <a href="/search/cs?searchtype=author&query=Majmundar%2C+A">Anuja Majmundar</a>, <a href="/search/cs?searchtype=author&query=Seo%2C+S">Sungyong Seo</a>, <a href="/search/cs?searchtype=author&query=Matsui%2C+A">Akira Matsui</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+S">Shen Yan</a>, <a href="/search/cs?searchtype=author&query=Allem%2C+J">Jon-Patrick Allem</a>, <a href="/search/cs?searchtype=author&query=Ferrara%2C+E">Emilio Ferrara</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1804.07886v1-abstract-short" style="display: inline;"> According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.07886v1-abstract-full').style.display = 'inline'; document.getElementById('1804.07886v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.07886v1-abstract-full" style="display: none;"> According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.07886v1-abstract-full').style.display = 'none'; document.getElementById('1804.07886v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 186-189 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.02149">arXiv:1804.02149</a> <span> [<a href="https://arxiv.org/pdf/1804.02149">pdf</a>, <a href="https://arxiv.org/ps/1804.02149">ps</a>, <a href="https://arxiv.org/format/1804.02149">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Context-aware Data Aggregation with Localized Information Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jiang%2C+B">Bo Jiang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1804.02149v3-abstract-short" style="display: inline;"> In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP rel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.02149v3-abstract-full').style.display = 'inline'; document.getElementById('1804.02149v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.02149v3-abstract-full" style="display: none;"> In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion by explicitly modeling the adversary's knowledge. However, it is stricter than $2蔚$-LDP and $蔚$-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. We then present an optimization framework for privacy-preserving data aggregation, with the goal of minimizing the expected squared error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under several models in closed-form. We then validate our analysis by {numerical analysis} using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.02149v3-abstract-full').style.display = 'none'; document.getElementById('1804.02149v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 15 figures, To appear in the processing of the IEEE Conference on Communications and Network Security, 30 May-1 June , 2018, Beijing, China</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1801.03494">arXiv:1801.03494</a> <span> [<a href="https://arxiv.org/pdf/1801.03494">pdf</a>, <a href="https://arxiv.org/format/1801.03494">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/e21111092">10.3390/e21111092 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Secure Retrospective Interference Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seif%2C+M">Mohamed Seif</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a>, <a href="/search/cs?searchtype=author&query=Li%2C+M">Ming Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1801.03494v3-abstract-short" style="display: inline;"> In this paper, the $K$-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging du… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.03494v3-abstract-full').style.display = 'inline'; document.getElementById('1801.03494v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.03494v3-abstract-full" style="display: none;"> In this paper, the $K$-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging due to the delayed nature of CSIT, and the distributed nature of the transmitters. Our scheme works over two phases: phase one in which each transmitter sends information symbols mixed with artificial noises, and repeats such transmission over multiple rounds. In the next phase, each transmitter uses delayed CSIT of the previous phase and sends a function of the net interference and artificial noises (generated in previous phase), which is simultaneously useful for all receivers. These phases are designed to ensure the decodability of the desired messages while satisfying the secrecy constraints. We present our achievable scheme for three models, namely: 1) $K$-user interference channel with confidential messages (IC-CM), and we show that $\frac{1}{2} (\sqrt{K} -6) $ SDoF is achievable, 2) $K$-user interference channel with an external eavesdropper (IC-EE), and 3) $K$-user IC with confidential messages and an external eavesdropper (IC-CM-EE). We show that for the $K$-user IC-EE, $\frac{1}{2} (\sqrt{K} -3) $ SDoF is achievable, and for the $K$-user IC-CM-EE, $\frac{1}{2} (\sqrt{K} -6) $ is achievable. To the best of our knowledge, this is the first result on the $K$-user interference channel with secrecy constrained models and delayed CSIT that achieves a SDoF which scales with $K$, the number of users. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.03494v3-abstract-full').style.display = 'none'; document.getElementById('1801.03494v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE Transactions on Wireless Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1801.01875">arXiv:1801.01875</a> <span> [<a href="https://arxiv.org/pdf/1801.01875">pdf</a>, <a href="https://arxiv.org/format/1801.01875">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Near Optimal Coded Data Shuffling for Distributed Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Attia%2C+M+A">Mohamed A. Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1801.01875v1-abstract-short" style="display: inline;"> Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of workers allows different nodes to obtain fresh data assignments at each learning epoch. This process has been shown to provide improvements in the learning process. However, the statistical benefits of distributed data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.01875v1-abstract-full').style.display = 'inline'; document.getElementById('1801.01875v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.01875v1-abstract-full" style="display: none;"> Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of workers allows different nodes to obtain fresh data assignments at each learning epoch. This process has been shown to provide improvements in the learning process. However, the statistical benefits of distributed data shuffling come at the cost of extra communication overhead from the master node to worker nodes, and can act as one of the major bottlenecks in the overall time for computation. There has been significant recent interest in devising approaches to minimize this communication overhead. One approach is to provision for extra storage at the computing nodes. The other emerging approach is to leverage coded communication to minimize the overall communication overhead. The focus of this work is to understand the fundamental trade-off between the amount of storage and the communication overhead for distributed data shuffling. In this work, we first present an information theoretic formulation for the data shuffling problem, accounting for the underlying problem parameters (number of workers, $K$, number of data points, $N$, and the available storage, $S$ per node). We then present an information theoretic lower bound on the communication overhead for data shuffling as a function of these parameters. We next present a novel coded communication scheme and show that the resulting communication overhead of the proposed scheme is within a multiplicative factor of at most $\frac{K}{K-1}$ from the information-theoretic lower bound. Furthermore, we present the aligned coded shuffling scheme for some storage values, which achieves the optimal storage vs communication trade-off for $K<5$, and further reduces the maximum multiplicative gap down to $\frac{K-\frac{1}{3}}{K-1}$, for $K\geq 5$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.01875v1-abstract-full').style.display = 'none'; document.getElementById('1801.01875v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.10430">arXiv:1711.10430</a> <span> [<a href="https://arxiv.org/pdf/1711.10430">pdf</a>, <a href="https://arxiv.org/format/1711.10430">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Online Edge Caching and Wireless Delivery in Fog-Aided Networks with Dynamic Content Popularity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Azimi%2C+S+M">Seyyed Mohammadreza Azimi</a>, <a href="/search/cs?searchtype=author&query=Simeone%2C+O">Osvaldo Simeone</a>, <a href="/search/cs?searchtype=author&query=Sengupta%2C+A">Avik Sengupta</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.10430v3-abstract-short" style="display: inline;"> Fog Radio Access Network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.10430v3-abstract-full').style.display = 'inline'; document.getElementById('1711.10430v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.10430v3-abstract-full" style="display: none;"> Fog Radio Access Network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this work focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs' caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit to the long-term NDT of F- RAN system. Analytical results are further verified by numerical simulation, yielding important design insights. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.10430v3-abstract-full').style.display = 'none'; document.getElementById('1711.10430v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 8 figures, Accepted for publication at IEEE Journal in Selected Areas in Communications, Special Issue on Caching for Communication Systems and Networks. arXiv admin note: text overlap with arXiv:1701.06188</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.08452">arXiv:1711.08452</a> <span> [<a href="https://arxiv.org/pdf/1711.08452">pdf</a>, <a href="https://arxiv.org/format/1711.08452">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Combating Computational Heterogeneity in Large-Scale Distributed Computing via Work Exchange </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Attia%2C+M+A">Mohamed A. Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.08452v1-abstract-short" style="display: inline;"> Owing to data-intensive large-scale applications, distributed computation systems have gained significant recent interest, due to their ability of running such tasks over a large number of commodity nodes in a time efficient manner. One of the major bottlenecks that adversely impacts the time efficiency is the computational heterogeneity of distributed nodes, often limiting the task completion tim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.08452v1-abstract-full').style.display = 'inline'; document.getElementById('1711.08452v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.08452v1-abstract-full" style="display: none;"> Owing to data-intensive large-scale applications, distributed computation systems have gained significant recent interest, due to their ability of running such tasks over a large number of commodity nodes in a time efficient manner. One of the major bottlenecks that adversely impacts the time efficiency is the computational heterogeneity of distributed nodes, often limiting the task completion time due to the slowest worker. In this paper, we first present a lower bound on the expected computation time based on the work-conservation principle. We then present our approach of work exchange to combat the latency problem, in which faster workers can be reassigned additional leftover computations that were originally assigned to slower workers. We present two variations of the work exchange approach: a) when the computational heterogeneity knowledge is known a priori; and b) when heterogeneity is unknown and is estimated in an online manner to assign tasks to distributed workers. As a baseline, we also present and analyze the use of an optimized Maximum Distance Separable (MDS) coded distributed computation scheme over heterogeneous nodes. Simulation results also compare the proposed approach of work exchange, the baseline MDS coded scheme and the lower bound obtained via work-conservation principle. We show that the work exchange scheme achieves time for computation which is very close to the lower bound with limited coordination and communication overhead even when the knowledge about heterogeneity levels is not available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.08452v1-abstract-full').style.display = 'none'; document.getElementById('1711.08452v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.05244">arXiv:1711.05244</a> <span> [<a href="https://arxiv.org/pdf/1711.05244">pdf</a>, <a href="https://arxiv.org/format/1711.05244">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Private Information Retrieval from Storage Constrained Databases -- Coded Caching meets PIR </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdul-Wahid%2C+M">Maryam Abdul-Wahid</a>, <a href="/search/cs?searchtype=author&query=Almoualem%2C+F">Firas Almoualem</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+D">Deepak Kumar</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.05244v1-abstract-short" style="display: inline;"> Private information retrieval (PIR) allows a user to retrieve a desired message out of $K$ possible messages from $N$ databases without revealing the identity of the desired message. Majority of existing works on PIR assume the presence of replicated databases, each storing all the $K$ messages. In this work, we consider the problem of PIR from storage constrained databases. Each database has a st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.05244v1-abstract-full').style.display = 'inline'; document.getElementById('1711.05244v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.05244v1-abstract-full" style="display: none;"> Private information retrieval (PIR) allows a user to retrieve a desired message out of $K$ possible messages from $N$ databases without revealing the identity of the desired message. Majority of existing works on PIR assume the presence of replicated databases, each storing all the $K$ messages. In this work, we consider the problem of PIR from storage constrained databases. Each database has a storage capacity of $渭KL$ bits, where $K$ is the number of messages, $L$ is the size of each message in bits, and $渭\in [1/N, 1]$ is the normalized storage. In the storage constrained PIR problem, there are two key design questions: a) how to store content across each database under storage constraints; and b) construction of schemes that allow efficient PIR through storage constrained databases. The main contribution of this work is a general achievable scheme for PIR from storage constrained databases for any value of storage. In particular, for any $(N,K)$, with normalized storage $渭= t/N$, where the parameter $t$ can take integer values $t \in \{1, 2, \ldots, N\}$, we show that our proposed PIR scheme achieves a download cost of $\left(1+ \frac{1}{t}+ \frac{1}{t^{2}}+ \cdots + \frac{1}{t^{K-1}}\right)$. The extreme case when $渭=1$ (i.e., $t=N$) corresponds to the setting of replicated databases with full storage. For this extremal setting, our scheme recovers the information-theoretically optimal download cost characterized by Sun and Jafar as $\left(1+ \frac{1}{N}+ \cdots + \frac{1}{N^{K-1}}\right)$. For the other extreme, when $渭= 1/N$ (i.e., $t=1$), the proposed scheme achieves a download cost of $K$. The interesting aspect of the result is that for intermediate values of storage, i.e., $1/N < 渭<1$, the proposed scheme can strictly outperform memory-sharing between extreme values of storage. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.05244v1-abstract-full').style.display = 'none'; document.getElementById('1711.05244v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1707.03858">arXiv:1707.03858</a> <span> [<a href="https://arxiv.org/pdf/1707.03858">pdf</a>, <a href="https://arxiv.org/format/1707.03858">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Gradient Coding from Cyclic MDS Codes and Expander Graphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raviv%2C+N">Netanel Raviv</a>, <a href="/search/cs?searchtype=author&query=Tamo%2C+I">Itzhak Tamo</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rashish Tandon</a>, <a href="/search/cs?searchtype=author&query=Dimakis%2C+A+G">Alexandros G. Dimakis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1707.03858v3-abstract-short" style="display: inline;"> Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient codi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.03858v3-abstract-full').style.display = 'inline'; document.getElementById('1707.03858v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1707.03858v3-abstract-full" style="display: none;"> Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient coding problem, in which we settle for approximate gradient computation instead of the exact one. This approach enables graceful degradation, i.e., the $\ell_2$ error of the approximate gradient is a decreasing function of the number of stragglers. Our main result is that normalized adjacency matrices of expander graphs yield excellent approximate gradient codes, which enable significantly less computation compared to exact gradient coding, and guarantee faster convergence than trivial solutions under standard assumptions. We experimentally test our approach on Amazon EC2, and show that the generalization error of approximate gradient coding is very close to the full gradient while requiring significantly less computation from the workers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.03858v3-abstract-full').style.display = 'none'; document.getElementById('1707.03858v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1706.07035">arXiv:1706.07035</a> <span> [<a href="https://arxiv.org/pdf/1706.07035">pdf</a>, <a href="https://arxiv.org/ps/1706.07035">ps</a>, <a href="https://arxiv.org/format/1706.07035">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> The Capacity of Cache Aided Private Information Retrieval </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1706.07035v1-abstract-short" style="display: inline;"> The problem of cache enabled private information retrieval (PIR) is considered in which a user wishes to privately retrieve one out of $K$ messages, each of size $L$ bits from $N$ distributed databases. The user has a local cache of storage $SL$ bits which can be used to store any function of the $K$ messages. The main contribution of this work is the exact characterization of the capacity of cach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.07035v1-abstract-full').style.display = 'inline'; document.getElementById('1706.07035v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1706.07035v1-abstract-full" style="display: none;"> The problem of cache enabled private information retrieval (PIR) is considered in which a user wishes to privately retrieve one out of $K$ messages, each of size $L$ bits from $N$ distributed databases. The user has a local cache of storage $SL$ bits which can be used to store any function of the $K$ messages. The main contribution of this work is the exact characterization of the capacity of cache aided PIR as a function of the storage parameter $S$. In particular, for a given cache storage parameter $S$, the information-theoretically optimal download cost $D^{*}(S)/L$ (or the inverse of capacity) is shown to be equal to $(1- \frac{S}{K})\left(1+ \frac{1}{N}+ \ldots + \frac{1}{N^{K-1}}\right)$. Special cases of this result correspond to the settings when $S=0$, for which the optimal download cost was shown by Sun and Jafar to be $\left(1+ \frac{1}{N}+ \ldots + \frac{1}{N^{K-1}}\right)$, and the case when $S=K$, i.e., cache size is large enough to store all messages locally, for which the optimal download cost is $0$. The intermediate points $S\in (0, K)$ can be readily achieved through a simple memory-sharing based PIR scheme. The key technical contribution of this work is the converse, i.e., a lower bound on the download cost as a function of storage $S$ which shows that memory sharing is information-theoretically optimal. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1706.07035v1-abstract-full').style.display = 'none'; document.getElementById('1706.07035v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.02108">arXiv:1705.02108</a> <span> [<a href="https://arxiv.org/pdf/1705.02108">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-030-03405-4_5">10.1007/978-3-030-03405-4_5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Optimizing noise level for perturbing geo-location data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Palia%2C+A">Abhinav Palia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rajat Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.02108v1-abstract-short" style="display: inline;"> With the tremendous increase in the number of smart phones, app stores have been overwhelmed with applications requiring geo-location access in order to provide their users better services through personalization. Revealing a user's location to these third party apps, no matter at what frequency, is a severe privacy breach which can have unpleasant social consequences. In order to prevent inferenc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.02108v1-abstract-full').style.display = 'inline'; document.getElementById('1705.02108v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.02108v1-abstract-full" style="display: none;"> With the tremendous increase in the number of smart phones, app stores have been overwhelmed with applications requiring geo-location access in order to provide their users better services through personalization. Revealing a user's location to these third party apps, no matter at what frequency, is a severe privacy breach which can have unpleasant social consequences. In order to prevent inference attacks derived from geo-location data, a number of location obfuscation techniques have been proposed in the literature. However, none of them provides any objective measure of privacy guarantee. Some work has been done to define differential privacy for geo-location data in the form of geo-indistinguishability with l privacy guarantee. These techniques do not utilize any prior background information about the Points of Interest (PoIs) of a user and apply Laplacian noise to perturb all the location coordinates. Intuitively, the utility of such a mechanism can be improved if the noise distribution is derived after considering some prior information about PoIs. In this paper, we apply the standard definition of differential privacy on geo-location data. We use first principles to model various privacy and utility constraints, prior background information available about the PoIs (distribution of PoI locations in a 1D plane) and the granularity of the input required by different types of apps, in order to produce a more accurate and a utility maximizing differentially private algorithm for geo-location data at the OS level. We investigate this for a particular category of apps and for some specific scenarios. This will also help us to verify that whether Laplacian noise is still the optimal perturbation when we have such prior information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.02108v1-abstract-full').style.display = 'none'; document.getElementById('1705.02108v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 May, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages and 3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> FICC 2018: Advances in Information and Communication Networks pp 63-73 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1704.02732">arXiv:1704.02732</a> <span> [<a href="https://arxiv.org/pdf/1704.02732">pdf</a>, <a href="https://arxiv.org/ps/1704.02732">ps</a>, <a href="https://arxiv.org/format/1704.02732">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Degrees of Freedom and Achievable Rate of Wide-Band Multi-cell Multiple Access Channels With No CSIT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeon%2C+Y">Yo-Seb Jeon</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+N">Namyoon Lee</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1704.02732v1-abstract-short" style="display: inline;"> This paper considers a $K$-cell multiple access channel with inter-symbol interference. The primary finding of this paper is that, without instantaneous channel state information at the transmitters (CSIT), the sum degrees-of-freedom (DoF) of the considered channel is $\frac{尾-1}尾K$ with $尾\geq 2$ when the number of users per cell is sufficiently large, where $尾$ is the ratio of the maximum channe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.02732v1-abstract-full').style.display = 'inline'; document.getElementById('1704.02732v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1704.02732v1-abstract-full" style="display: none;"> This paper considers a $K$-cell multiple access channel with inter-symbol interference. The primary finding of this paper is that, without instantaneous channel state information at the transmitters (CSIT), the sum degrees-of-freedom (DoF) of the considered channel is $\frac{尾-1}尾K$ with $尾\geq 2$ when the number of users per cell is sufficiently large, where $尾$ is the ratio of the maximum channel-impulse-response (CIR) length of desired links to that of interfering links in each cell. Our finding implies that even without instantaneous CSIT, \textit{interference-free DoF per cell} is achievable as $尾$ approaches infinity with a sufficiently large number of users per cell. This achievability is shown by a blind interference management method that exploits the relativity in delay spreads between desired and interfering links. In this method, all inter-cell-interference signals are aligned to the same direction by using a discrete-Fourier-transform-based precoding with cyclic prefix that only depends on the number of CIR taps. Using this method, we also characterize the achievable sum rate of the considered channel, in a closed-form expression. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.02732v1-abstract-full').style.display = 'none'; document.getElementById('1704.02732v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE Transactions on Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.06188">arXiv:1701.06188</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Online Edge Caching in Fog-Aided Wireless Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Azimi%2C+S+M">Seyyed Mohammadreza Azimi</a>, <a href="/search/cs?searchtype=author&query=Simeone%2C+O">Osvaldo Simeone</a>, <a href="/search/cs?searchtype=author&query=Sengupta%2C+A">Avik Sengupta</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1701.06188v4-abstract-short" style="display: inline;"> In a Fog Radio Access Network (F-RAN) architecture, edge nodes (ENs), such as base stations, are equipped with limited-capacity caches, as well as with fronthaul links that can support given transmission rates from a cloud processor. Existing information-theoretic analyses of content delivery in F-RANs have focused on offline caching with separate content placement and delivery phases. In contrast… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.06188v4-abstract-full').style.display = 'inline'; document.getElementById('1701.06188v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.06188v4-abstract-full" style="display: none;"> In a Fog Radio Access Network (F-RAN) architecture, edge nodes (ENs), such as base stations, are equipped with limited-capacity caches, as well as with fronthaul links that can support given transmission rates from a cloud processor. Existing information-theoretic analyses of content delivery in F-RANs have focused on offline caching with separate content placement and delivery phases. In contrast, this work considers an online caching set-up, in which the set of popular files is time-varying and both cache replenishment and content delivery can take place in each time slot. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to- noise ratio regime. Online caching and delivery schemes based on reactive and proactive caching are investigated, and their performance is compared to optimal offline caching schemes both analytically and via numerical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.06188v4-abstract-full').style.display = 'none'; document.getElementById('1701.06188v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 5 figures, Please see the updated version arXiv:1711.10430</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1612.03301">arXiv:1612.03301</a> <span> [<a href="https://arxiv.org/pdf/1612.03301">pdf</a>, <a href="https://arxiv.org/format/1612.03301">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> </div> </div> <p class="title is-5 mathjax"> Gradient Coding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Rashish Tandon</a>, <a href="/search/cs?searchtype=author&query=Lei%2C+Q">Qi Lei</a>, <a href="/search/cs?searchtype=author&query=Dimakis%2C+A+G">Alexandros G. Dimakis</a>, <a href="/search/cs?searchtype=author&query=Karampatziakis%2C+N">Nikos Karampatziakis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1612.03301v2-abstract-short" style="display: inline;"> We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for Synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and gener… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1612.03301v2-abstract-full').style.display = 'inline'; document.getElementById('1612.03301v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1612.03301v2-abstract-full" style="display: none;"> We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for Synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and generalization error. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1612.03301v2-abstract-full').style.display = 'none'; document.getElementById('1612.03301v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 December, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1609.09823">arXiv:1609.09823</a> <span> [<a href="https://arxiv.org/pdf/1609.09823">pdf</a>, <a href="https://arxiv.org/format/1609.09823">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On the Worst-case Communication Overhead for Distributed Data Shuffling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Attia%2C+M">Mohamed Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1609.09823v1-abstract-short" style="display: inline;"> Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.09823v1-abstract-full').style.display = 'inline'; document.getElementById('1609.09823v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.09823v1-abstract-full" style="display: none;"> Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process. This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers. In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem. We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing. Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers. We also present an information theoretic lower bound on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.09823v1-abstract-full').style.display = 'none'; document.getElementById('1609.09823v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in Allerton 2016</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1609.05181">arXiv:1609.05181</a> <span> [<a href="https://arxiv.org/pdf/1609.05181">pdf</a>, <a href="https://arxiv.org/ps/1609.05181">ps</a>, <a href="https://arxiv.org/format/1609.05181">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Information Theoretic Limits of Data Shuffling for Distributed Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Attia%2C+M">Mohamed Attia</a>, <a href="/search/cs?searchtype=author&query=Tandon%2C+R">Ravi Tandon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1609.05181v1-abstract-short" style="display: inline;"> Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process. In each iteration, different shuffled data points are assigned by a central node to a distributed set of workers to perform local computations, which leads to communication bottlenecks. The focus of this paper is on formalizing and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.05181v1-abstract-full').style.display = 'inline'; document.getElementById('1609.05181v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1609.05181v1-abstract-full" style="display: none;"> Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process. In each iteration, different shuffled data points are assigned by a central node to a distributed set of workers to perform local computations, which leads to communication bottlenecks. The focus of this paper is on formalizing and understanding the fundamental information-theoretic trade-off between storage (per worker) and the worst-case communication overhead for the data shuffling problem. We completely characterize the information theoretic trade-off for $K=2$, and $K=3$ workers, for any value of storage capacity, and show that increasing the storage across workers can reduce the communication overhead by leveraging coding. We propose a novel and systematic data delivery and storage update strategy for each data shuffle iteration, which preserves the structural properties of the storage across the workers, and aids in minimizing the communication overhead in subsequent data shuffling iterations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1609.05181v1-abstract-full').style.display = 'none'; document.getElementById('1609.05181v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To be presented at IEEE GLOBECOM, December 2016</span> </p> </li> </ol> <nav class="pagination is-small 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