Enhanced Security Verifiable Secure Aggregation Scheme in Federated Learning

Enhanced Security Verifiable Secure Aggregation Scheme 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 Enhanced Security Verifiable Secure Aggregation Scheme in Federated Learning Wujun Yao, Yiliang Han, Tanping Zhou, Xiaolin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6827986/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted 12 You are reading this latest preprint version Abstract Federated Learning(FL) enables multiple participants to build a loosely coupled distributed machine learning system under the coordination of a central server. Existing FL models typically assume that the server aggregating data is semi-honest, but this assumption does not align with the complexities of real-world application environments, where the server may carry out collusion attacks or replay attacks. VerifyNet is a representative federated learning protocol for verifiable secure aggregation. In this paper, we analyze the security of VerifyNet, identify two shortcomings: low tolerance to collusion attacks and inability to resist combinatorial replay attacks. Furthermore, we have experimentally confirmed the existence of these two security vulnerabilities. To address the issue of low tolerance for collusion attacks, we have constructed a secure homomorphic hash function key generator using a randomized approach to prevent malicious servers from obtaining shared keys and forging data. To address the issue of being unable to resist replay attacks, we have constructed a secure additional verification information generation algorithm using AES-CTR encryption mode, which prevents malicious servers from obtaining increments from historical data and constructing combinatorial replay attacks. Security analysis shows that our scheme effectively achieves privacy protection and aggregation verification. We tested the performance of the scheme in a local area network environment. Experimental data indicates that when the number of clients is 500 and the number of gradients per client is 5000, our scheme only requires an additional 5.76‰ computational overhead and 3.46% communication overhead compared to the VerifyNet protocol, and eliminates the security vulnerabilities of collusion attacks and combinatorial replay attacks. Federated Learning Verifiable Secure Aggregation Replay Attack Re-sistance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted Editorial decision: Revision requested 23 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 06 Jun, 2025 First submitted to journal 05 Jun, 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-6827986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471425197,"identity":"034d0087-e79a-4c9b-b8c3-228381360b54","order_by":0,"name":"Wujun Yao","email":"","orcid":"","institution":"Engineering University of People’s Armed Police","correspondingAuthor":false,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Yao","suffix":""},{"id":471425198,"identity":"df736773-2725-4b77-abd2-6f526f333299","order_by":1,"name":"Yiliang Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACAxCRUGDDw8/fQJIWgzQZyRkHSNHCYHDYxqAhgUgt5uxnj0k8MDjPY8BwgPHDxxwitFj25KVJJBjc5jFnbmCWnLmNGIcdyDEDa7FsOMDGzEuUlvNvQFrO8RgcSCBWyw2wLQdI0vLG2CLBIJlHcsbBZiL9cj7H8OaPCjt7fv7mgx8+EqMFCFgkIDRjA3HqgYD5A9FKR8EoGAWjYGQCABAYNG0+2G7pAAAAAElFTkSuQmCC","orcid":"","institution":"Engineering University of People’s Armed Police","correspondingAuthor":true,"prefix":"","firstName":"Yiliang","middleName":"","lastName":"Han","suffix":""},{"id":471425199,"identity":"b412a2ed-0a27-40e4-bf99-1b251fd0d35e","order_by":2,"name":"Tanping Zhou","email":"","orcid":"","institution":"Engineering University of People’s Armed Police","correspondingAuthor":false,"prefix":"","firstName":"Tanping","middleName":"","lastName":"Zhou","suffix":""},{"id":471425200,"identity":"fe9c37bb-ab54-4b54-ad8d-3722e7253796","order_by":3,"name":"Xiaolin Wang","email":"","orcid":"","institution":"Engineering University of People’s Armed Police","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-05 10:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6827986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6827986/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12083-025-02111-8","type":"published","date":"2025-10-23T16:16:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94489942,"identity":"4ba2389c-b711-4455-85a8-dd6fe5f165d0","added_by":"auto","created_at":"2025-10-27 17:06:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1101763,"visible":true,"origin":"","legend":"","description":"","filename":"EnhancedSecurityVerifiableSecureAggregationSchemeinFederatedLearning20250605PeertoPeerNetworkingandApplications.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6827986/v1_covered_2b9f0141-9891-4e2c-957d-d337e93f4d77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Security Verifiable Secure Aggregation Scheme 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":"peer-to-peer-networking-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ppna","sideBox":"Learn more about [Peer-to-Peer Networking and Applications](http://link.springer.com/journal/12083)","snPcode":"12083","submissionUrl":"https://submission.nature.com/new-submission/12083/3","title":"Peer-to-Peer Networking and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Federated Learning, Verifiable Secure Aggregation, Replay Attack Re-sistance","lastPublishedDoi":"10.21203/rs.3.rs-6827986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6827986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Federated Learning(FL) enables multiple participants to build a loosely coupled distributed machine learning system under the coordination of a central server. 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