An Efficient Differentially-Private Weighted Support Vector Machine Algorithm with Noisy Gradient Descent

An Efficient Differentially-Private Weighted Support Vector Machine Algorithm with Noisy Gradient Descent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Efficient Differentially-Private Weighted Support Vector Machine Algorithm with Noisy Gradient Descent Shuvo Chandra Pall, Hafiz Imtiaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6803468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The proliferation of digital data has led to significant opportunities for analysis and strategic initiatives, where sensitive personal details is being utilized. Unfortunately, this has led to growing privacy concerns over time. Differential privacy (DP) is the most widely used framework to resolve the aforementioned privacy issues. The DP guarantee can be achieved by incorporating randomness into the algorithm pipeline. However, generalized differentially private empirical risk minimization (DP-ERM) algorithms typically employ the output perturbation or objective perturbation approaches, which do not perform well when the ambient dimension of data is large. Additionally, weighted support vector machine (wSVM), which has unique applications in individualized treatment rules and outcome-weighted learning (OWL) suffers from low utility if output perturbation or objective perturbation is employed. To address these challenges, a differentially-private wSVM algorithm has been developed in this work that incorporates gradient perturbation into the optimization process of wSVM. The performance and the privacy-utility tradeoff of the proposed approach are evaluated and compared with that of the existing DP-wSVM algorithm on three publicly available real datasets. The proposed algorithm is shown to ensure an accurate and efficient classification performance in pharmacogenetics and OWL applications with a more balanced tradeoff between privacy and utility, irrespective of ambient data dimension. Additionally, a theoretical analysis of the privacy guarantee of the proposed algorithm has been performed, which paves the way of a tight accounting of the overall privacy budget using Rényi Differential Privacy. The proposed algorithm achieves a close approximation of the performance of the non-private algorithm, while ensuring strict privacy. Last but not the least, the proposed algorithm achieves better results compared to the existing differentially private wSVM approach. Differential privacy (DP) weighted support vector machine (wSVM) Output perturbation Noisy gradient descent Rényi differential privacy Privacy - utility tradeof Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6803468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470215140,"identity":"e701d5d1-8dd7-47f4-adcf-c5241a9b1efa","order_by":0,"name":"Shuvo Chandra Pall","email":"","orcid":"","institution":"Bangladesh University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuvo","middleName":"Chandra","lastName":"Pall","suffix":""},{"id":470215146,"identity":"055f538b-4919-4304-9de0-42131a86ae12","order_by":1,"name":"Hafiz Imtiaz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2PvWrDMBCATwjkxU1WZWlfQZmMoeRZFATuEkihS4ZQNHky6eon6awQcJZCVhcvDgHPzlI8tGlP6c9SOy2dOugDnQ7dfegOwOH4hwggpcEIDDB7f/kqyQ6FCqtwVKj4UIj+SbE3x8P4r5TAW5Flc31/2/PW1ayeXULA1bqE+QhL7UqYKDBcFJz5MnpMHyII02isIVNYKtsHM1NjhFVAZsVZvAKRT4YaGC6Yd+yy2YGRVulv45uXwyt2TvcaDieUHAczVuGKUaJt54RoEp9SdrDUohjEvKKDJFN+mFTDdLywSccuG0X3zXPRv7i72tbNfHQeeKqs6yebtP/yDf8Y5WficDgcjj/xBl8cYjCpFONtAAAAAElFTkSuQmCC","orcid":"","institution":"Bangladesh University of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hafiz","middleName":"","lastName":"Imtiaz","suffix":""}],"badges":[],"createdAt":"2025-06-02 15:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6803468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6803468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94243385,"identity":"da1423f5-e5b4-4e4a-ba72-de0e86e98c12","added_by":"auto","created_at":"2025-10-24 04:38:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":647585,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803468/v1_covered_42f5e36e-c010-41bd-8cd0-3924293cbff4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Differentially-Private Weighted Support Vector Machine Algorithm with Noisy Gradient Descent","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Differential privacy (DP), weighted support vector machine (wSVM), Output perturbation, Noisy gradient descent, Rényi differential privacy, Privacy - utility tradeof","lastPublishedDoi":"10.21203/rs.3.rs-6803468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6803468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe proliferation of digital data has led to significant opportunities for analysis and strategic initiatives, where sensitive personal details is being utilized. 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