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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Fuller, B"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <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/2407.00217">arXiv:2407.00217</a> <span> [<a href="https://arxiv.org/pdf/2407.00217">pdf</a>, <a href="https://arxiv.org/ps/2407.00217">ps</a>, <a href="https://arxiv.org/format/2407.00217">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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"> A flexured-gimbal 3-axis force-torque sensor reveals minimal cross-axis coupling in an insect-sized flapping-wing robot </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Weber%2C+A">Aaron Weber</a>, <a href="/search/cs?searchtype=author&query=Dhingra%2C+D">Daksh Dhingra</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+S+B">Sawyer B. Fuller</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="2407.00217v1-abstract-short" style="display: inline;"> The mechanical complexity of flapping wings, their unsteady aerodynamic flow, and challenge of making measurements at the scale of a sub-gram flapping-wing flying insect robot (FIR) make its behavior hard to predict. Knowing the precise mapping from voltage input to torque output, however, can be used to improve their mechanical and flight controller design. To address this challenge, we created a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00217v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00217v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00217v1-abstract-full" style="display: none;"> The mechanical complexity of flapping wings, their unsteady aerodynamic flow, and challenge of making measurements at the scale of a sub-gram flapping-wing flying insect robot (FIR) make its behavior hard to predict. Knowing the precise mapping from voltage input to torque output, however, can be used to improve their mechanical and flight controller design. To address this challenge, we created a sensitive force-torque sensor based on a flexured gimbal that only requires a standard motion capture system or accelerometer for readout. Our device precisely and accurately measures pitch and roll torques simultaneously, as well as thrust, on a tethered flapping-wing FIR in response to changing voltage input signals. With it, we were able to measure cross-axis coupling of both torque and thrust input commands on a 180 mg FIR, the UW Robofly. We validated these measurements using free-flight experiments. Our results showed that roll and pitch have maximum cross-axis coupling errors of 8.58% and 17.24%, respectively, relative to the range of torque that is possible. Similarly, varying the pitch and roll commands resulted in up to a 5.78% deviation from the commanded thrust, across the entire commanded torque range. Our system, the first to measure two torque axes simultaneously, shows that torque commands have a negligible cross-axis coupling on both torque and thrust. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00217v1-abstract-full').style.display = 'none'; document.getElementById('2407.00217v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.20061">arXiv:2406.20061</a> <span> [<a href="https://arxiv.org/pdf/2406.20061">pdf</a>, <a href="https://arxiv.org/format/2406.20061">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Modeling and LQR Control of Insect Sized Flapping Wing Robot </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dhingra%2C+D">Daksh Dhingra</a>, <a href="/search/cs?searchtype=author&query=Kaheman%2C+K">Kadierdan Kaheman</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+S+B">Sawyer B. Fuller</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="2406.20061v1-abstract-short" style="display: inline;"> Flying insects can perform rapid, sophisticated maneuvers like backflips, sharp banked turns, and in-flight collision recovery. To emulate these in aerial robots weighing less than a gram, known as flying insect robots (FIRs), a fast and responsive control system is essential. To date, these have largely been, at their core, elaborations of proportional-integral-derivative (PID)-type feedback cont… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.20061v1-abstract-full').style.display = 'inline'; document.getElementById('2406.20061v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.20061v1-abstract-full" style="display: none;"> Flying insects can perform rapid, sophisticated maneuvers like backflips, sharp banked turns, and in-flight collision recovery. To emulate these in aerial robots weighing less than a gram, known as flying insect robots (FIRs), a fast and responsive control system is essential. To date, these have largely been, at their core, elaborations of proportional-integral-derivative (PID)-type feedback control. Without exception, their gains have been painstakingly tuned by hand. Aggressive maneuvers have further required task-specific tuning. Optimal control has the potential to mitigate these issues, but has to date only been demonstrated using approxiate models and receding horizon controllers (RHC) that are too computationally demanding to be carried out onboard the robot. Here we used a more accurate stroke-averaged model of forces and torques to implement the first demonstration of optimal control on an FIR that is computationally efficient enough to be performed by a microprocessor carried onboard. We took force and torque measurements from a 150 mg FIR, the UW Robofly, using a custom-built sensitive force-torque sensor, and validated them using motion capture data in free flight. We demonstrated stable hovering (RMS error of about 4 cm) and trajectory tracking maneuvers at translational velocities up to 25 cm/s using an optimal linear quadratic regulator (LQR). These results were enabled by a more accurate model and lay the foundation for future work that uses our improved model and optimal controller in conjunction with recent advances in low-power receding horizon control to perform accurate aggressive maneuvers without iterative, task-specific tuning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.20061v1-abstract-full').style.display = 'none'; document.getElementById('2406.20061v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">The video of the results can be accessed using www.youtube.com/watch?v=0o7j1nS2KHA</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.12241">arXiv:2404.12241</a> <span> [<a href="https://arxiv.org/pdf/2404.12241">pdf</a>, <a href="https://arxiv.org/format/2404.12241">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Introducing v0.5 of the AI Safety Benchmark from MLCommons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vidgen%2C+B">Bertie Vidgen</a>, <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Adarsh Agrawal</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+A+M">Ahmed M. Ahmed</a>, <a href="/search/cs?searchtype=author&query=Akinwande%2C+V">Victor Akinwande</a>, <a href="/search/cs?searchtype=author&query=Al-Nuaimi%2C+N">Namir Al-Nuaimi</a>, <a href="/search/cs?searchtype=author&query=Alfaraj%2C+N">Najla Alfaraj</a>, <a href="/search/cs?searchtype=author&query=Alhajjar%2C+E">Elie Alhajjar</a>, <a href="/search/cs?searchtype=author&query=Aroyo%2C+L">Lora Aroyo</a>, <a href="/search/cs?searchtype=author&query=Bavalatti%2C+T">Trupti Bavalatti</a>, <a href="/search/cs?searchtype=author&query=Bartolo%2C+M">Max Bartolo</a>, <a href="/search/cs?searchtype=author&query=Blili-Hamelin%2C+B">Borhane Blili-Hamelin</a>, <a href="/search/cs?searchtype=author&query=Bollacker%2C+K">Kurt Bollacker</a>, <a href="/search/cs?searchtype=author&query=Bomassani%2C+R">Rishi Bomassani</a>, <a href="/search/cs?searchtype=author&query=Boston%2C+M+F">Marisa Ferrara Boston</a>, <a href="/search/cs?searchtype=author&query=Campos%2C+S">Sim茅on Campos</a>, <a href="/search/cs?searchtype=author&query=Chakra%2C+K">Kal Chakra</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C">Canyu Chen</a>, <a href="/search/cs?searchtype=author&query=Coleman%2C+C">Cody Coleman</a>, <a href="/search/cs?searchtype=author&query=Coudert%2C+Z+D">Zacharie Delpierre Coudert</a>, <a href="/search/cs?searchtype=author&query=Derczynski%2C+L">Leon Derczynski</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+D">Debojyoti Dutta</a>, <a href="/search/cs?searchtype=author&query=Eisenberg%2C+I">Ian Eisenberg</a>, <a href="/search/cs?searchtype=author&query=Ezick%2C+J">James Ezick</a>, <a href="/search/cs?searchtype=author&query=Frase%2C+H">Heather Frase</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Brian Fuller</a> , et al. (75 additional authors not shown) </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="2404.12241v2-abstract-short" style="display: inline;"> This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-pu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12241v2-abstract-full').style.display = 'inline'; document.getElementById('2404.12241v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.12241v2-abstract-full" style="display: none;"> This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.12241v2-abstract-full').style.display = 'none'; document.getElementById('2404.12241v2-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.06515">arXiv:2402.06515</a> <span> [<a href="https://arxiv.org/pdf/2402.06515">pdf</a>, <a href="https://arxiv.org/ps/2402.06515">ps</a>, <a href="https://arxiv.org/format/2402.06515">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"> The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Pai%2C+R">Rashmi Pai</a>, <a href="/search/cs?searchtype=author&query=Russell%2C+A">Alexander Russell</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="2402.06515v4-abstract-short" style="display: inline;"> Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impeded by both efficiency concerns and the fact they offer statistical, rather than absolute, conclusions. We attend to both of these difficulties, defining new families of audits that improve efficiency and offer qualitative… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06515v4-abstract-full').style.display = 'inline'; document.getElementById('2402.06515v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.06515v4-abstract-full" style="display: none;"> Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impeded by both efficiency concerns and the fact they offer statistical, rather than absolute, conclusions. We attend to both of these difficulties, defining new families of audits that improve efficiency and offer qualitative advances in statistical power. Our new audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations rather than a single decision; this can reflect the presence of marginal marks, which appear regularly on hand-marked ballots. We show that this simple expedient can offer significant efficiency improvements with only minor changes to existing auditing infrastructure. We consider two ways of representing these marks, both yield risk-limiting comparison audits in the formal sense of Fuller, Harrison, and Russell (IEEE Security & Privacy 2023). We then define a new type of post-election audit we call a contested audit. These permit each candidate to provide a cast-vote record table advancing their own claim to victory. We prove that these audits offer remarkable sample efficiency, yielding control of risk with a constant number of samples (that is independent of margin). This is a first for an audit with provable soundness. These results are formulated in a game-based security model that specify quantitative soundness and completeness guarantees. These audits provide a means to handle contestation of election results affirmed by conventional RLAs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06515v4-abstract-full').style.display = 'none'; document.getElementById('2402.06515v4-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">27 pages, full version of an article of the same name at USENIX Security 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/2312.06674">arXiv:2312.06674</a> <span> [<a href="https://arxiv.org/pdf/2312.06674">pdf</a>, <a href="https://arxiv.org/format/2312.06674">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Inan%2C+H">Hakan Inan</a>, <a href="/search/cs?searchtype=author&query=Upasani%2C+K">Kartikeya Upasani</a>, <a href="/search/cs?searchtype=author&query=Chi%2C+J">Jianfeng Chi</a>, <a href="/search/cs?searchtype=author&query=Rungta%2C+R">Rashi Rungta</a>, <a href="/search/cs?searchtype=author&query=Iyer%2C+K">Krithika Iyer</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Y">Yuning Mao</a>, <a href="/search/cs?searchtype=author&query=Tontchev%2C+M">Michael Tontchev</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+Q">Qing Hu</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Brian Fuller</a>, <a href="/search/cs?searchtype=author&query=Testuggine%2C+D">Davide Testuggine</a>, <a href="/search/cs?searchtype=author&query=Khabsa%2C+M">Madian Khabsa</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="2312.06674v1-abstract-short" style="display: inline;"> We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a valuable tool for categorizing a specific set of safety risks found in LLM prompts (i.e., prompt classification). This taxonomy is also instrumental in classifying the responses generated by LLMs to these prompts, a process we refer to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06674v1-abstract-full').style.display = 'inline'; document.getElementById('2312.06674v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.06674v1-abstract-full" style="display: none;"> We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a valuable tool for categorizing a specific set of safety risks found in LLM prompts (i.e., prompt classification). This taxonomy is also instrumental in classifying the responses generated by LLMs to these prompts, a process we refer to as response classification. For the purpose of both prompt and response classification, we have meticulously gathered a dataset of high quality. Llama Guard, a Llama2-7b model that is instruction-tuned on our collected dataset, albeit low in volume, demonstrates strong performance on existing benchmarks such as the OpenAI Moderation Evaluation dataset and ToxicChat, where its performance matches or exceeds that of currently available content moderation tools. Llama Guard functions as a language model, carrying out multi-class classification and generating binary decision scores. Furthermore, the instruction fine-tuning of Llama Guard allows for the customization of tasks and the adaptation of output formats. This feature enhances the model's capabilities, such as enabling the adjustment of taxonomy categories to align with specific use cases, and facilitating zero-shot or few-shot prompting with diverse taxonomies at the input. We are making Llama Guard model weights available and we encourage researchers to further develop and adapt them to meet the evolving needs of the community for AI safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.06674v1-abstract-full').style.display = 'none'; document.getElementById('2312.06674v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.11647">arXiv:2309.11647</a> <span> [<a href="https://arxiv.org/pdf/2309.11647">pdf</a>, <a href="https://arxiv.org/format/2309.11647">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</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"> Potential and limitations of random Fourier features for dequantizing quantum machine learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sweke%2C+R">Ryan Sweke</a>, <a href="/search/cs?searchtype=author&query=Recio-Armengol%2C+E">Erik Recio-Armengol</a>, <a href="/search/cs?searchtype=author&query=Jerbi%2C+S">Sofiene Jerbi</a>, <a href="/search/cs?searchtype=author&query=Gil-Fuster%2C+E">Elies Gil-Fuster</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Bryce Fuller</a>, <a href="/search/cs?searchtype=author&query=Eisert%2C+J">Jens Eisert</a>, <a href="/search/cs?searchtype=author&query=Meyer%2C+J+J">Johannes Jakob Meyer</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="2309.11647v3-abstract-short" style="display: inline;"> Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via random Fourier features (RFF). In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11647v3-abstract-full').style.display = 'inline'; document.getElementById('2309.11647v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.11647v3-abstract-full" style="display: none;"> Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via random Fourier features (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11647v3-abstract-full').style.display = 'none'; document.getElementById('2309.11647v3-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, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">44 pages (33+11). 6 Figures, with many clarifying figures added to this version from original version. Comments and feedback welcome. Now accepted in Quantum - this is the final version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.09288">arXiv:2307.09288</a> <span> [<a href="https://arxiv.org/pdf/2307.09288">pdf</a>, <a href="https://arxiv.org/format/2307.09288">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Llama 2: Open Foundation and Fine-Tuned Chat Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Touvron%2C+H">Hugo Touvron</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+L">Louis Martin</a>, <a href="/search/cs?searchtype=author&query=Stone%2C+K">Kevin Stone</a>, <a href="/search/cs?searchtype=author&query=Albert%2C+P">Peter Albert</a>, <a href="/search/cs?searchtype=author&query=Almahairi%2C+A">Amjad Almahairi</a>, <a href="/search/cs?searchtype=author&query=Babaei%2C+Y">Yasmine Babaei</a>, <a href="/search/cs?searchtype=author&query=Bashlykov%2C+N">Nikolay Bashlykov</a>, <a href="/search/cs?searchtype=author&query=Batra%2C+S">Soumya Batra</a>, <a href="/search/cs?searchtype=author&query=Bhargava%2C+P">Prajjwal Bhargava</a>, <a href="/search/cs?searchtype=author&query=Bhosale%2C+S">Shruti Bhosale</a>, <a href="/search/cs?searchtype=author&query=Bikel%2C+D">Dan Bikel</a>, <a href="/search/cs?searchtype=author&query=Blecher%2C+L">Lukas Blecher</a>, <a href="/search/cs?searchtype=author&query=Ferrer%2C+C+C">Cristian Canton Ferrer</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+M">Moya Chen</a>, <a href="/search/cs?searchtype=author&query=Cucurull%2C+G">Guillem Cucurull</a>, <a href="/search/cs?searchtype=author&query=Esiobu%2C+D">David Esiobu</a>, <a href="/search/cs?searchtype=author&query=Fernandes%2C+J">Jude Fernandes</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+J">Jeremy Fu</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+W">Wenyin Fu</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Brian Fuller</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+C">Cynthia Gao</a>, <a href="/search/cs?searchtype=author&query=Goswami%2C+V">Vedanuj Goswami</a>, <a href="/search/cs?searchtype=author&query=Goyal%2C+N">Naman Goyal</a>, <a href="/search/cs?searchtype=author&query=Hartshorn%2C+A">Anthony Hartshorn</a>, <a href="/search/cs?searchtype=author&query=Hosseini%2C+S">Saghar Hosseini</a> , et al. (43 additional authors not shown) </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="2307.09288v2-abstract-short" style="display: inline;"> In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.09288v2-abstract-full').style.display = 'inline'; document.getElementById('2307.09288v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.09288v2-abstract-full" style="display: none;"> In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.09288v2-abstract-full').style.display = 'none'; document.getElementById('2307.09288v2-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 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 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/2304.09899">arXiv:2304.09899</a> <span> [<a href="https://arxiv.org/pdf/2304.09899">pdf</a>, <a href="https://arxiv.org/format/2304.09899">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</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/QCE57702.2023.00036">10.1109/QCE57702.2023.00036 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantum Kernel Alignment with Stochastic Gradient Descent </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gentinetta%2C+G">Gian Gentinetta</a>, <a href="/search/cs?searchtype=author&query=Sutter%2C+D">David Sutter</a>, <a href="/search/cs?searchtype=author&query=Zoufal%2C+C">Christa Zoufal</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Bryce Fuller</a>, <a href="/search/cs?searchtype=author&query=Woerner%2C+S">Stefan Woerner</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="2304.09899v1-abstract-short" style="display: inline;"> Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set -- a task called kernel alignment. In this paper we study this problem using the Pegasos algorithm, which is an algorithm that uses stochastic gradient descent to solve the support vector m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09899v1-abstract-full').style.display = 'inline'; document.getElementById('2304.09899v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.09899v1-abstract-full" style="display: none;"> Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set -- a task called kernel alignment. In this paper we study this problem using the Pegasos algorithm, which is an algorithm that uses stochastic gradient descent to solve the support vector machine optimization problem. We extend Pegasos to the quantum case and and demonstrate its effectiveness for kernel alignment. Unlike previous work which performs kernel alignment by training a QSVM within an outer optimization loop, we show that using Pegasos it is possible to simultaneously train the support vector machine and align the kernel. Our experiments show that this approach is capable of aligning quantum feature maps with high accuracy, and outperforms existing quantum kernel alignment techniques. Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09899v1-abstract-full').style.display = 'none'; document.getElementById('2304.09899v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">10 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.11020">arXiv:2209.11020</a> <span> [<a href="https://arxiv.org/pdf/2209.11020">pdf</a>, <a href="https://arxiv.org/format/2209.11020">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+S">Sohaib Ahmad</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Mahmood%2C+K">Kaleel Mahmood</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="2209.11020v1-abstract-short" style="display: inline;"> Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11020v1-abstract-full').style.display = 'inline'; document.getElementById('2209.11020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.11020v1-abstract-full" style="display: none;"> Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss. Our attacks are black-box, requiring no knowledge of the weights of the target neural network, only the dimension, and values of the output vector. To show the versatility of the alignment loss, we apply our attack framework to the task of membership inference (Shokri et al., IEEE S&P 2017) on biometric data. For the iris, membership inference attack against classification networks improves from 52% to 62% accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11020v1-abstract-full').style.display = 'none'; document.getElementById('2209.11020v1-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 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">This is a major revision of a paper titled "Inverting Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models" by the same authors that appears at IJCB 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/2202.02607">arXiv:2202.02607</a> <span> [<a href="https://arxiv.org/pdf/2202.02607">pdf</a>, <a href="https://arxiv.org/format/2202.02607">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"> Adaptive Risk-Limiting Ballot Comparison Audits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Harrison%2C+A">Abigail Harrison</a>, <a href="/search/cs?searchtype=author&query=Russell%2C+A">Alexander Russell</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="2202.02607v5-abstract-short" style="display: inline;"> Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The most ballot efficient approaches proceed by "ballot comparison." However, ballot comparison requ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.02607v5-abstract-full').style.display = 'inline'; document.getElementById('2202.02607v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.02607v5-abstract-full" style="display: none;"> Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The most ballot efficient approaches proceed by "ballot comparison." However, ballot comparison requires an untrusted declaration of the contents of each cast ballot, rather than a simple tabulation of vote totals. This "cast-vote record table" (CVR) is then spot-checked against ballots for consistency. In many practical settings, the cost of generating a suitable CVR dominates the cost of conducting the audit, preventing widespread adoption of these sample-efficient techniques. We introduce a new RLA procedure: an "adaptive ballot comparison" audit. In this audit, a global CVR is never produced; instead, a three-stage procedure is iterated: 1) a batch is selected, 2) a CVR is produced for that batch, and 3) a ballot within the batch is sampled, inspected by auditors, and compared with the CVR. We prove that such an audit can achieve risk commensurate with standard comparison audits while generating a fraction of the CVR. We present three main contributions: 1) a formal adversarial model for RLAs; 2) definition and analysis of an adaptive audit procedure with rigorous risk limits and an associated correctness analysis accounting for the incidental errors arising in typical audits; and 3) an analysis of practical efficiency. This method can be organized in rounds (as is typical for comparison audits) where sampled CVRs are produced in parallel. Using data from Florida's 2020 presidential election with 5% risk and 1% margin, only 22% of the CVR is generated; at 10% margin, only 2% is generated. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.02607v5-abstract-full').style.display = 'none'; document.getElementById('2202.02607v5-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 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">33 pages. Substantial technical and editorial revision</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.08247">arXiv:2102.08247</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shukla%2C+P">Priyesh Shukla</a>, <a href="/search/cs?searchtype=author&query=Muralidhar%2C+A">Ankith Muralidhar</a>, <a href="/search/cs?searchtype=author&query=Iliev%2C+N">Nick Iliev</a>, <a href="/search/cs?searchtype=author&query=Tulabandhula%2C+T">Theja Tulabandhula</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+S+B">Sawyer B. Fuller</a>, <a href="/search/cs?searchtype=author&query=Trivedi%2C+A+R">Amit Ranjan Trivedi</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="2102.08247v2-abstract-short" style="display: inline;"> We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approaches rely on the three-dimensional (3D) Gaussian Mixture Model (GMM)-based representation of a 3D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.08247v2-abstract-full').style.display = 'inline'; document.getElementById('2102.08247v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.08247v2-abstract-full" style="display: none;"> We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approaches rely on the three-dimensional (3D) Gaussian Mixture Model (GMM)-based representation of a 3D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy -- thereby, limiting flying duration and/or size of the drone due to a larger battery. Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3D map representation using a harmonic mean of "Gaussian-like" mixture (HMGM) model. The likelihood function useful for drone localization can be efficiently implemented by connecting many multi-input inverters in parallel, each programmed with the parameters of the 3D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D indoor localization dataset. The average localization error in our approach is $\sim$0.1125 m which is only slightly degraded than software-based evaluation ($\sim$0.08 m). Meanwhile, our localization framework is ultra-low-power, consuming as little as $\sim$17 $渭$W power while processing a depth frame in 1.33 ms over hundred pose hypotheses in the particle-filtering (PF) algorithm used to localize the drone. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.08247v2-abstract-full').style.display = 'none'; document.getElementById('2102.08247v2-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> 24 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">Will submit the revised article.</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> B.7; I.2.9 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.15850">arXiv:2007.15850</a> <span> [<a href="https://arxiv.org/pdf/2007.15850">pdf</a>, <a href="https://arxiv.org/format/2007.15850">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Resist : Reconstruction of irises from templates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+S">Sohaib Ahmad</a>, <a href="/search/cs?searchtype=author&query=Geiger%2C+C">Christopher Geiger</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</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="2007.15850v2-abstract-short" style="display: inline;"> Iris recognition systems transform an iris image into a feature vector. The seminal pipeline segments an image into iris and non-iris pixels, normalizes this region into a fixed-dimension rectangle, and extracts features which are stored and called a template (Daugman, 2009). This template is stored on a system. A future reading of an iris can be transformed and compared against template vectors t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.15850v2-abstract-full').style.display = 'inline'; document.getElementById('2007.15850v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.15850v2-abstract-full" style="display: none;"> Iris recognition systems transform an iris image into a feature vector. The seminal pipeline segments an image into iris and non-iris pixels, normalizes this region into a fixed-dimension rectangle, and extracts features which are stored and called a template (Daugman, 2009). This template is stored on a system. A future reading of an iris can be transformed and compared against template vectors to determine or verify the identity of an individual. As templates are often stored together, they are a valuable target to an attacker. We show how to invert templates across a variety of iris recognition systems. That is, we show how to transform templates into realistic looking iris images that are also deemed as the same iris by the corresponding recognition system. Our inversion is based on a convolutional neural network architecture we call RESIST (REconStructing IriSes from Templates). We apply RESIST to a traditional Gabor filter pipeline, to a DenseNet (Huang et al., CVPR 2017) feature extractor, and to a DenseNet architecture that works without normalization. Both DenseNet feature extractors are based on the recent ThirdEye recognition system (Ahmad and Fuller, BTAS 2019). When training and testing using the ND-0405 dataset, reconstructed images demonstrate a rank-1 accuracy of 100%, 76%, and 96% respectively for the three pipelines. The core of our approach is similar to an autoencoder. However, standalone training the core produced low accuracy. The final architecture integrates into an generative adversarial network (Goodfellow et al., NeurIPS, 2014) producing higher accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.15850v2-abstract-full').style.display = 'none'; document.getElementById('2007.15850v2-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 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.07921">arXiv:1910.07921</a> <span> [<a href="https://arxiv.org/pdf/1910.07921">pdf</a>, <a href="https://arxiv.org/format/1910.07921">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> FASHION: Functional and Attack graph Secured HybrId Optimization of virtualized Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Callahan%2C+D">Devon Callahan</a>, <a href="/search/cs?searchtype=author&query=Curry%2C+T">Timothy Curry</a>, <a href="/search/cs?searchtype=author&query=Davidson%2C+H">Hazel Davidson</a>, <a href="/search/cs?searchtype=author&query=Zitoun%2C+H">Heytem Zitoun</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Michel%2C+L">Laurent Michel</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="1910.07921v3-abstract-short" style="display: inline;"> Maintaining a resilient computer network is a delicate task with conflicting priorities. Flows should be served while controlling risk due to attackers. Upon publication of a vulnerability, administrators scramble to manually mitigate risk while waiting for a patch. We introduce FASHION: a linear optimizer that balances routing flows with the security risk posed by these flows. FASHION formalize… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07921v3-abstract-full').style.display = 'inline'; document.getElementById('1910.07921v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.07921v3-abstract-full" style="display: none;"> Maintaining a resilient computer network is a delicate task with conflicting priorities. Flows should be served while controlling risk due to attackers. Upon publication of a vulnerability, administrators scramble to manually mitigate risk while waiting for a patch. We introduce FASHION: a linear optimizer that balances routing flows with the security risk posed by these flows. FASHION formalizes routing as a multi-commodity flow problem with side constraints. FASHION formulates security using two approximations of risk in a probabilistic attack graph (Frigault et al., Network Security Metrics 2017). FASHION's output is a set of software-defined networking rules consumable by Frenetic (Foster et al., ICFP 2011). We introduce a topology generation tool that creates data center network instances including flows and vulnerabilities. FASHION is executed on instances of up to 600 devices, thousands of flows, and million edge attack graphs. Solve time averages 30 minutes on the largest instances (seconds on the smallest instances). To ensure the security objective is accurate, the output solution is assessed using risk as defined by Frigault et al. FASHION allows enterprises to reconfigure their network in response to changes in functionality or security requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.07921v3-abstract-full').style.display = 'none'; document.getElementById('1910.07921v3-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">Accepted to IEEE TDSC. This version adds an online evaluation Sections 4.2 and 5.2.4</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2.1; C.2.3; G.1.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.06147">arXiv:1907.06147</a> <span> [<a href="https://arxiv.org/pdf/1907.06147">pdf</a>, <a href="https://arxiv.org/format/1907.06147">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> ThirdEye: Triplet Based Iris Recognition without Normalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+S">Sohaib Ahmad</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</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="1907.06147v1-abstract-short" style="display: inline;"> Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for go… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.06147v1-abstract-full').style.display = 'inline'; document.getElementById('1907.06147v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.06147v1-abstract-full" style="display: none;"> Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for good accuracy.We ask if normalization is beneficial? Towards answering this question, we develop a new iris recognition system called ThirdEye based on triplet convolutional neural networks (Schroff et al., ICCV 2015). ThirdEye directly uses segmented images without normalization. We observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. For IITD, the most constrained dataset, this improves on the best prior work. However, for ND-0405 and UbirisV2,our equal error rate is slightly worse than prior systems. Our concluding hypothesis is that normalization is more important for less constrained environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.06147v1-abstract-full').style.display = 'none'; document.getElementById('1907.06147v1-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 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.10210">arXiv:1906.10210</a> <span> [<a href="https://arxiv.org/pdf/1906.10210">pdf</a>, <a href="https://arxiv.org/format/1906.10210">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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/10.1371/journal.pone.0238267">10.1371/journal.pone.0238267 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A laser-microfabricated electrohydrodynamic thruster for centimeter-scale aerial robots </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Prasad%2C+H+K+H">Hari Krishna Hari Prasad</a>, <a href="/search/cs?searchtype=author&query=Vaddi%2C+R+S">Ravi Sankar Vaddi</a>, <a href="/search/cs?searchtype=author&query=Chukewad%2C+Y+M">Yogesh M Chukewad</a>, <a href="/search/cs?searchtype=author&query=Dedic%2C+E">Elma Dedic</a>, <a href="/search/cs?searchtype=author&query=Novosselov%2C+I">Igor Novosselov</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+S+B">Sawyer B Fuller</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="1906.10210v4-abstract-short" style="display: inline;"> To date, insect scale robots capable of controlled flight have used flapping wings for generating lift, but this requires a complex and failure-prone mechanism. A simpler alternative is electrohydrodynamic (EHD) thrust, which requires no moving mechanical parts. In EHD, corona discharge generates a flow of ions in an electric field between two electrodes; the high-velocity ions transfer their kine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.10210v4-abstract-full').style.display = 'inline'; document.getElementById('1906.10210v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.10210v4-abstract-full" style="display: none;"> To date, insect scale robots capable of controlled flight have used flapping wings for generating lift, but this requires a complex and failure-prone mechanism. A simpler alternative is electrohydrodynamic (EHD) thrust, which requires no moving mechanical parts. In EHD, corona discharge generates a flow of ions in an electric field between two electrodes; the high-velocity ions transfer their kinetic energy to neutral air molecules through collisions, accelerating the gas and creating thrust. We introduce a fabrication process for EHD thruster based on 355 nm laser micromachining and our approach allows for greater flexibility in materials selection. Our four-thruster device measures 1.8 x 2.5 cm and is composed of steel emitters and a lightweight carbon fiber mesh. The current and thrust characteristics of each individual thruster of the quad thruster is determined and agrees with Townsend relation. The mass of the quad thruster is 37 mg and the measured thrust is greater than its weight (362.6 uN). The robot is able to lift off at a voltage of 4.6 kV with a thrust to weight ratio of 1.38. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.10210v4-abstract-full').style.display = 'none'; document.getElementById('1906.10210v4-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 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 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">Co-primary authors: Hari Krishna Hari Prasad, Ravi Sankar Vaddi, and Yogesh M Chukewad Submitted to PLOS ONE</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> PLoS ONE 15(4): e0231362 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.05988">arXiv:1902.05988</a> <span> [<a href="https://arxiv.org/pdf/1902.05988">pdf</a>, <a href="https://arxiv.org/format/1902.05988">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="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> DOCSDN: Dynamic and Optimal Configuration of Software-Defined Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Curry%2C+T">Timothy Curry</a>, <a href="/search/cs?searchtype=author&query=Callahan%2C+D">Devon Callahan</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Michel%2C+L">Laurent Michel</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="1902.05988v1-abstract-short" style="display: inline;"> Networks are designed with functionality, security, performance, and cost in mind. Tools exist to check or optimize individual properties of a network. These properties may conflict, so it is not always possible to run these tools in series to find a configuration that meets all requirements. This leads to network administrators manually searching for a configuration. This need not be the case.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05988v1-abstract-full').style.display = 'inline'; document.getElementById('1902.05988v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.05988v1-abstract-full" style="display: none;"> Networks are designed with functionality, security, performance, and cost in mind. Tools exist to check or optimize individual properties of a network. These properties may conflict, so it is not always possible to run these tools in series to find a configuration that meets all requirements. This leads to network administrators manually searching for a configuration. This need not be the case. In this paper, we introduce a layered framework for optimizing network configuration for functional and security requirements. Our framework is able to output configurations that meet reachability, bandwidth, and risk requirements. Each layer of our framework optimizes over a single property. A lower layer can constrain the search problem of a higher layer allowing the framework to converge on a joint solution. Our approach has the most promise for software-defined networks which can easily reconfigure their logical configuration. Our approach is validated with experiments over the fat tree topology, which is commonly used in data center networks. Search terminates in between 1-5 minutes in experiments. Thus, our solution can propose new configurations for short term events such as defending against a focused network attack. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.05988v1-abstract-full').style.display = 'none'; document.getElementById('1902.05988v1-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 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.08245">arXiv:1812.08245</a> <span> [<a href="https://arxiv.org/pdf/1812.08245">pdf</a>, <a href="https://arxiv.org/format/1812.08245">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Unconstrained Iris Segmentation using Convolutional Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahmad%2C+S">Sohaib Ahmad</a>, <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</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="1812.08245v1-abstract-short" style="display: inline;"> The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.08245v1-abstract-full').style.display = 'inline'; document.getElementById('1812.08245v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.08245v1-abstract-full" style="display: none;"> The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained in a single environment and tested in multiple environments. Our network builds on the Mask R-CNN framework (He et al., ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network. Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings. For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.08245v1-abstract-full').style.display = 'none'; document.getElementById('1812.08245v1-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, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.02014">arXiv:1703.02014</a> <span> [<a href="https://arxiv.org/pdf/1703.02014">pdf</a>, <a href="https://arxiv.org/ps/1703.02014">ps</a>, <a href="https://arxiv.org/format/1703.02014">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"> SoK: Cryptographically Protected Database Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fuller%2C+B">Benjamin Fuller</a>, <a href="/search/cs?searchtype=author&query=Varia%2C+M">Mayank Varia</a>, <a href="/search/cs?searchtype=author&query=Yerukhimovich%2C+A">Arkady Yerukhimovich</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+E">Emily Shen</a>, <a href="/search/cs?searchtype=author&query=Hamlin%2C+A">Ariel Hamlin</a>, <a href="/search/cs?searchtype=author&query=Gadepally%2C+V">Vijay Gadepally</a>, <a href="/search/cs?searchtype=author&query=Shay%2C+R">Richard Shay</a>, <a href="/search/cs?searchtype=author&query=Mitchell%2C+J+D">John Darby Mitchell</a>, <a href="/search/cs?searchtype=author&query=Cunningham%2C+R+K">Robert K. Cunningham</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="1703.02014v2-abstract-short" style="display: inline;"> Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.02014v2-abstract-full').style.display = 'inline'; document.getElementById('1703.02014v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.02014v2-abstract-full" style="display: none;"> Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.02014v2-abstract-full').style.display = 'none'; document.getElementById('1703.02014v2-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 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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, to appear to IEEE Security and Privacy</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 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