Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning

Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning | 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 Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning Wujun Yao, Tanping Zhou, Yiliang Han, Xiaolin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6090375/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2025 Read the published version in Discover Computing → Version 1 posted 15 You are reading this latest preprint version Abstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environment. In reality, servers might be malicious, potentially tampering with or forging aggregation results. To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks. Initially, we construct a functional reuse private key ring generation algorithm, enabling clients to encrypt and protect their private gradients using the private key ring. Subsequently, leveraging the discrete logarithm difficulty problem, we devise a commitment protocol where clients commit to their encrypted private gradients. Upon receiving the aggregation result from the server, they collaboratively unlock the commitment, thereby verifying the aggregation result. Security analysis demonstrates that our solution effectively ensures privacy protection. We simulated consumer electronic products on the Raspberry Pi and tested the performance of the solution. Experimental data reveals that, with 100 clients, our scheme demonstrates that the overhead for proof generation and verification computations are 39.9% and 34.1% of the existing scheme, respectively, highlighting its lightweight nature. Federated Learning Verifiable Aggregation Privacy Protection Consumer Electronics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2025 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 14 May, 2025 Reviews received at journal 14 May, 2025 Reviews received at journal 09 May, 2025 Reviews received at journal 06 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviews received at journal 30 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 23 Apr, 2025 Editor invited by journal 23 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 22 Apr, 2025 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. 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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-6090375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451702963,"identity":"d7e4a0b7-be90-4fb2-8f10-2085b9409a37","order_by":0,"name":"Wujun Yao","email":"","orcid":"","institution":"Chinese People's Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Yao","suffix":""},{"id":451702964,"identity":"6994828b-a59b-4a76-a960-f7ac4eee95eb","order_by":1,"name":"Tanping Zhou","email":"","orcid":"","institution":"Chinese People's Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Tanping","middleName":"","lastName":"Zhou","suffix":""},{"id":451702965,"identity":"2d3254b0-a2cd-4877-91cf-c6b5cf5368ed","order_by":2,"name":"Yiliang Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACAxCRUGDDw8/fQJIWgzQZyRkHSNHCYHDYxqAhgUgt5uxnj0k8MDjPY8BwgPHDxxwitFj25KVJJBjc5jFnbmCWnLmNGIcdyDEDa7FsOMDGzEuUlvNvQFrO8RgcSCBWyw2wLQdI0vLG2CLBIJlHcsbBZiL9cj7H8OaPCjt7fv7mgx8+EqMFCFgkIDRjA3HqgYD5A9FKR8EoGAWjYGQCABAYNG0+2G7pAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese People's Armed Police Force Engineering University","correspondingAuthor":true,"prefix":"","firstName":"Yiliang","middleName":"","lastName":"Han","suffix":""},{"id":451702966,"identity":"707c747b-57af-4710-a39f-15a8e8164a9f","order_by":3,"name":"Xiaolin Wang","email":"","orcid":"","institution":"Chinese People's Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-02-23 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6090375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6090375/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10791-025-09676-1","type":"published","date":"2025-08-20T16:29:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847877,"identity":"38c82ae0-81a2-4c5b-b499-ad8becf09628","added_by":"auto","created_at":"2025-08-25 16:44:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1098722,"visible":true,"origin":"","legend":"","description":"","filename":"FormatedmanuscriptVerifiableSecureAggregationSchemeforPrivacyProtectioninFederatedLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6090375/v1_covered_d5018395-a44e-4819-be40-752a2100dc5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Federated Learning, Verifiable Aggregation, Privacy Protection, Consumer Electronics","lastPublishedDoi":"10.21203/rs.3.rs-6090375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6090375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Federated learning enables multiple participants to construct a distributed machine learning system coordinated by server. 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