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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="Adir, A"> <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" 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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/2410.09457">arXiv:2410.09457</a> <span> [<a href="https://arxiv.org/pdf/2410.09457">pdf</a>, <a href="https://arxiv.org/format/2410.09457">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> </div> </div> <p class="title is-5 mathjax"> Power-Softmax: Towards Secure LLM Inference over Encrypted Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zimerman%2C+I">Itamar Zimerman</a>, <a href="/search/cs?searchtype=author&query=Adir%2C+A">Allon Adir</a>, <a href="/search/cs?searchtype=author&query=Aharoni%2C+E">Ehud Aharoni</a>, <a href="/search/cs?searchtype=author&query=Avitan%2C+M">Matan Avitan</a>, <a href="/search/cs?searchtype=author&query=Baruch%2C+M">Moran Baruch</a>, <a href="/search/cs?searchtype=author&query=Drucker%2C+N">Nir Drucker</a>, <a href="/search/cs?searchtype=author&query=Lerner%2C+J">Jenny Lerner</a>, <a href="/search/cs?searchtype=author&query=Masalha%2C+R">Ramy Masalha</a>, <a href="/search/cs?searchtype=author&query=Meiri%2C+R">Reut Meiri</a>, <a href="/search/cs?searchtype=author&query=Soceanu%2C+O">Omri Soceanu</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="2410.09457v1-abstract-short" style="display: inline;"> Modern cryptographic methods for implementing privacy-preserving LLMs such as Homomorphic Encryption (HE) require the LLMs to have a polynomial form. Forming such a representation is challenging because Transformers include non-polynomial components, such as Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, whic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09457v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09457v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09457v1-abstract-full" style="display: none;"> Modern cryptographic methods for implementing privacy-preserving LLMs such as Homomorphic Encryption (HE) require the LLMs to have a polynomial form. Forming such a representation is challenging because Transformers include non-polynomial components, such as Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, which are less efficient over HE, or replaced non-polynomial components with easier-to-approximate primitives before training, e.g., Softmax with pointwise attention. The latter approach might introduce scalability challenges. We present a new HE-friendly variant of self-attention that offers a stable form for training and is easy to approximate with polynomials for secure inference. Our work introduces the first polynomial LLMs with 32 layers and over a billion parameters, exceeding the size of previous models by more than tenfold. The resulting models demonstrate reasoning and in-context learning (ICL) capabilities comparable to standard transformers of the same size, representing a breakthrough in the field. Finally, we provide a detailed latency breakdown for each computation over encrypted data, paving the way for further optimization, and explore the differences in inductive bias between transformers relying on our HE-friendly variant and standard transformers. Our code is attached as a supplement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09457v1-abstract-full').style.display = 'none'; document.getElementById('2410.09457v1-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 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">ACM Class:</span> F.2.2; I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.14284">arXiv:2203.14284</a> <span> [<a href="https://arxiv.org/pdf/2203.14284">pdf</a>, <a href="https://arxiv.org/format/2203.14284">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 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/10.1007/978-3-031-16815-4_22">10.1007/978-3-031-16815-4_22 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Privacy-preserving record linkage using local sensitive hash and private set intersection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Adir%2C+A">Allon Adir</a>, <a href="/search/cs?searchtype=author&query=Aharoni%2C+E">Ehud Aharoni</a>, <a href="/search/cs?searchtype=author&query=Drucker%2C+N">Nir Drucker</a>, <a href="/search/cs?searchtype=author&query=Kushnir%2C+E">Eyal Kushnir</a>, <a href="/search/cs?searchtype=author&query=Masalha%2C+R">Ramy Masalha</a>, <a href="/search/cs?searchtype=author&query=Mirkin%2C+M">Michael Mirkin</a>, <a href="/search/cs?searchtype=author&query=Soceanu%2C+O">Omri Soceanu</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="2203.14284v1-abstract-short" style="display: inline;"> The amount of data stored in data repositories increases every year. This makes it challenging to link records between different datasets across companies and even internally, while adhering to privacy regulations. Address or name changes, and even different spelling used for entity data, can prevent companies from using private deduplication or record-linking solutions such as private set interse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.14284v1-abstract-full').style.display = 'inline'; document.getElementById('2203.14284v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.14284v1-abstract-full" style="display: none;"> The amount of data stored in data repositories increases every year. This makes it challenging to link records between different datasets across companies and even internally, while adhering to privacy regulations. Address or name changes, and even different spelling used for entity data, can prevent companies from using private deduplication or record-linking solutions such as private set intersection (PSI). To this end, we propose a new and efficient privacy-preserving record linkage (PPRL) protocol that combines PSI and local sensitive hash (LSH) functions, and runs in linear time. We explain the privacy guarantees that our protocol provides and demonstrate its practicality by executing the protocol over two datasets with $2^{20}$ records each, in $11-45$ minutes, depending on network settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.14284v1-abstract-full').style.display = 'none'; document.getElementById('2203.14284v1-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 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.01805">arXiv:2011.01805</a> <span> [<a href="https://arxiv.org/pdf/2011.01805">pdf</a>, <a href="https://arxiv.org/format/2011.01805">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="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/10.56553/popets-2023-0020">10.56553/popets-2023-0020 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aharoni%2C+E">Ehud Aharoni</a>, <a href="/search/cs?searchtype=author&query=Adir%2C+A">Allon Adir</a>, <a href="/search/cs?searchtype=author&query=Baruch%2C+M">Moran Baruch</a>, <a href="/search/cs?searchtype=author&query=Drucker%2C+N">Nir Drucker</a>, <a href="/search/cs?searchtype=author&query=Ezov%2C+G">Gilad Ezov</a>, <a href="/search/cs?searchtype=author&query=Farkash%2C+A">Ariel Farkash</a>, <a href="/search/cs?searchtype=author&query=Greenberg%2C+L">Lev Greenberg</a>, <a href="/search/cs?searchtype=author&query=Masalha%2C+R">Ramy Masalha</a>, <a href="/search/cs?searchtype=author&query=Moshkowich%2C+G">Guy Moshkowich</a>, <a href="/search/cs?searchtype=author&query=Murik%2C+D">Dov Murik</a>, <a href="/search/cs?searchtype=author&query=Shaul%2C+H">Hayim Shaul</a>, <a href="/search/cs?searchtype=author&query=Soceanu%2C+O">Omri Soceanu</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="2011.01805v3-abstract-short" style="display: inline;"> Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01805v3-abstract-full').style.display = 'inline'; document.getElementById('2011.01805v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.01805v3-abstract-full" style="display: none;"> Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01805v3-abstract-full').style.display = 'none'; document.getElementById('2011.01805v3-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">17 pages, 7 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> E.1; 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