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Evaluation of Privacy-aware Support Vector Machine (SVM) Learning using Homomorphic Encryption
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To overcome these privacy issues, we can apply Fully Homomorphic Encryption (FHE) to encrypt data before it is fed into a machine learning model. This involves creating a homomorphic encryption key pair, and where the associated public key will be used to encrypt the input data, and the private key will decrypt the output. But, there is often a performance hit when we use homomorphic encryption, and so this paper evaluates the performance overhead of using the SVM machine learning technique with the OpenFHE homomorphic encryption library. This uses Python and the scikit-learn library for its implementation. The experiments include a range of variables such as multiplication depth, scale size, first modulus size, security level, batch size, and ring dimension, along with two different SVM models, SVM-Poly and SVM-Linear. Overall, the results show that the two main parameters which affect performance are the ring dimension and the modulus size, and that SVM-Poly and SVM-Linear show similar performance levels." /> <meta property="article:section" content="IMPLEMENTATION" /> <meta property="article:modified_time" content="2025-03-10T18:18:09+00:00" /> <meta property="article:published_time" content="2025-03-04T20:01:14+00:00" /> <meta property="article:tag" content="homomorphic encryption" /> <meta property="article:tag" content="Support Vector Machine" /> <meta property="article:tag" content="privacy-aware" /> </head> <body> <noscript> <h1 class="text-center">What a lovely hat</h1> <h4 class="text-center">Is it made out of <a href="https://iacr.org/tinfoil.html">tin foil</a>?</h4> </noscript> <div class="fixed-top" id="topNavbar"> <nav class="navbar navbar-custom navbar-expand-lg"> <div class="container px-0 justify-content-between justify-content-lg-evenly"> <div class="order-0 align-items-center 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class="authorName">William J Buchanan</span><a class="ms-1" target="_blank" href="https://orcid.org/0000-0003-0809-3523"><img class="align-baseline orcidIcon" src="/img/orcid.svg"></a><span class="affiliation">, Edinburgh Napier University</span></div> <div class="author"><span class="authorName">Hisham Ali</span><a class="ms-1" target="_blank" href="https://orcid.org/0000-0002-0333-4757"><img class="align-baseline orcidIcon" src="/img/orcid.svg"></a><span class="affiliation">, Edinburgh Napier University</span></div> <h5 class="mt-3">Abstract</h5> <p style="white-space: pre-wrap;">The requirement for privacy-aware machine learning increases as we continue to use PII (Personally Identifiable Information) within machine training. To overcome these privacy issues, we can apply Fully Homomorphic Encryption (FHE) to encrypt data before it is fed into a machine learning model. This involves creating a homomorphic encryption key pair, and where the associated public key will be used to encrypt the input data, and the private key will decrypt the output. But, there is often a performance hit when we use homomorphic encryption, and so this paper evaluates the performance overhead of using the SVM machine learning technique with the OpenFHE homomorphic encryption library. This uses Python and the scikit-learn library for its implementation. The experiments include a range of variables such as multiplication depth, scale size, first modulus size, security level, batch size, and ring dimension, along with two different SVM models, SVM-Poly and SVM-Linear. Overall, the results show that the two main parameters which affect performance are the ring dimension and the modulus size, and that SVM-Poly and SVM-Linear show similar performance levels.</p> </div> <div id="metadata" class="col-md-5 col-lg-4 ps-md-5 mt-4 mt-md-0"> <h5>Metadata</h5> <dl> <dt> Available format(s) </dt> <dd> <a class="btn btn-sm btn-outline-dark" href="/2025/417.pdf"> <img class="icon" src="/img/file-pdf.svg">PDF</a> </dd> <dt>Category</dt> <dd><a href="/search?category=IMPLEMENTATION"><small class="badge category category-IMPLEMENTATION">Implementation</small></a></dd> <dt>Publication info</dt> <dd>Preprint. </dd> <dt>Keywords</dt> <dd class="keywords"><a href="/search?q=homomorphic%20encryption" class="me-2 badge bg-secondary keyword">homomorphic encryption</a><a href="/search?q=Support%20Vector%20Machine" class="me-2 badge bg-secondary keyword">Support Vector Machine</a><a href="/search?q=privacy-aware" class="me-2 badge bg-secondary keyword">privacy-aware</a></dd> <dt>Contact author(s)</dt> <dd><span class="font-monospace"> b buchanan<span class="obfuscate"> @ </span>napier ac uk<br>h ali<span class="obfuscate"> @ </span>napier ac uk </span> </dd> <dt>History</dt> <dd>2025-03-10: revised</dd> <dd>2025-03-04: received</dd> <dd><a rel="nofollow" href="/archive/versions/2025/417">See all versions</a></dd> <dt>Short URL</dt> <dd><a href="https://ia.cr/2025/417">https://ia.cr/2025/417</a></dd> <dt>License</dt> <dd><a rel="license" target="_blank" href="https://creativecommons.org/licenses/by/4.0/"> <img class="licenseImg" src="/img/license/CC_BY.svg" alt="Creative Commons Attribution" title="Creative Commons Attribution"><br> <small>CC BY</small> </a> </dd> </dl> </div> </div> <p class="mt-4"><strong>BibTeX</strong> <button id="bibcopy" class="ms-2 btn btn-sm btn-outline-dark" aria-label="Copy to clipboard" onclick="copyBibtex()"> <img src="/img/copy-outline.svg" class="icon">Copy to clipboard</button></p> <pre id="bibtex"> @misc{cryptoeprint:2025/417, author = {William J Buchanan and Hisham Ali}, title = {Evaluation of Privacy-aware Support Vector Machine ({SVM}) Learning using Homomorphic Encryption}, howpublished = {Cryptology {ePrint} Archive, Paper 2025/417}, year = {2025}, url = {https://eprint.iacr.org/2025/417} } </pre> <script> var bibcopy; function triggerTooltip() { console.log('setting tooltip'); } window.onload = triggerTooltip; function copyBibtex() { let range = document.createRange(); range.selectNode(document.getElementById('bibtex')); window.getSelection().removeAllRanges(); window.getSelection().addRange(range); document.execCommand('copy'); window.getSelection().removeAllRanges(); let bibcopy = document.getElementById('bibcopy'); let copyTooltip = new bootstrap.Tooltip(bibcopy, {trigger: 'manual', title: 'Copied!'}); copyTooltip.show(); setTimeout(function() { copyTooltip.dispose(); }, 2000); } </script> </main> <div class="container-fluid mt-auto" id="eprintFooter"> <a href="https://iacr.org/"> <img id="iacrlogo" src="/img/iacrlogo_small.png" class="img-fluid d-block mx-auto" alt="IACR Logo"> </a> <div class="colorDiv"></div> <div class="alert alert-success w-75 mx-auto"> Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content. </div> </div> <script src="/css/bootstrap/js/bootstrap.bundle.min.js"></script> <script> var topNavbar = document.getElementById('topNavbar'); if (topNavbar) { document.addEventListener('scroll', function(e) { if (window.scrollY > 100) { topNavbar.classList.add('scrolled'); } else { topNavbar.classList.remove('scrolled'); } }) } </script> </body> </html>