Privacy-Preserving Patent Matching Model

Privacy-Preserving Patent Matching Model | 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 Article Privacy-Preserving Patent Matching Model Jiangtao Li, Wenlong Feng, Mengxing Huang, Siling Feng, Fu Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9081554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract To address the problems of privacy leakage in patent matching and low matching accuracy, this paper proposes a hybrid homomorphic encryption model (HyMix-HE) for patent similarity matching. First, the user’s patent requirement vector is encrypted using CKKS, and floating-point similarities are computed in batch in the ciphertext domain of the requirements to obtain preliminary patent matches. Second, a δ safety band mechanism is designed to concentrate the samples that require exact review in the truly uncertain neighborhood, together with a secure-domain switching mechanism that converts ciphertexts from the CKKS approximate domain to the BFV fixed-point domain under minimal leakage. Finally, similarity computation over ciphertext is performed in BFV on these critical samples to achieve accurate patent review. Meanwhile, lightweight auditing is implemented on Hyperledger Fabric using commitment/hash techniques, enabling recomputation and accountability without exposing any plaintext. Based on modeled simulations and comparative experiments, the proposed method is evaluated against pure-CKKS and pure-BFV schemes. The results show that, while preserving privacy, the proposed model achieves a good balance between efficiency and accuracy and overall outperforms single-scheme baselines, thus effectively supporting privacy-preserving in patent trading scenarios. Physical sciences/Engineering Physical sciences/Mathematics and computing homomorphic encryption privacy-preserving computation patent matching HyMix-HE Hyperledger Fabric Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 14 Mar, 2026 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. 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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-9081554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617576950,"identity":"ed05e220-beee-4dbc-963a-3e25c354ac98","order_by":0,"name":"Jiangtao Li","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Jiangtao","middleName":"","lastName":"Li","suffix":""},{"id":617576951,"identity":"506da00a-1058-44b3-aca2-1db90a41e6dd","order_by":1,"name":"Wenlong Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACfvbGxgcJBjYJDMwwoQMEtEj2HD5s8KEijQQtBjfS0gRnnDmcgBAirCXHjJm37Xwefzvvwc+FbQxyfDcSGD8X4HPYmTdmj3nbbhdLHOZLlp7ZxmAseSOBWXoGHi18x3PMjYFaEhsO8xhI87YxJG64kcDGzIPPZQdyzIAqzyXOP8xj/BuopZ6gFoETaWmSM84cSNxwmAeklyHBgJAWaCAnJ24EarHmOSdhOPPMw2ZpfFqgUWmXOO/8GePbPGU28nzHkw9+xusXNCABxIwNJGgYBaNgFIyCUYANAAAfrVBW382ImgAAAABJRU5ErkJggg==","orcid":"","institution":"Hainan University","correspondingAuthor":true,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Feng","suffix":""},{"id":617576952,"identity":"3a1e704c-c6c5-4e0d-9e62-1095614dd574","order_by":2,"name":"Mengxing Huang","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Mengxing","middleName":"","lastName":"Huang","suffix":""},{"id":617576953,"identity":"af547f54-8b40-4b6b-8d9d-dadecdac24dd","order_by":3,"name":"Siling Feng","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Siling","middleName":"","lastName":"Feng","suffix":""},{"id":617576954,"identity":"171d4567-ae0e-4f39-bf28-7c9948f0e623","order_by":4,"name":"Fu Gao","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Fu","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2026-03-10 08:54:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9081554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9081554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403433,"identity":"736a7fa9-a5fb-47e4-affb-267a53d4acf6","added_by":"auto","created_at":"2026-04-08 09:14:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148356,"visible":true,"origin":"","legend":"","description":"","filename":"PrivacyPreservingPatentMatchingModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9081554/v1_covered_013c3165-8d5c-448c-99a0-6a048dcab932.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Privacy-Preserving Patent Matching Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"homomorphic encryption, privacy-preserving computation, patent matching, HyMix-HE, Hyperledger Fabric","lastPublishedDoi":"10.21203/rs.3.rs-9081554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9081554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the problems of privacy leakage in patent matching and low matching accuracy, this paper proposes a hybrid homomorphic encryption model (HyMix-HE) for patent similarity matching. First, the user\u0026rsquo;s patent requirement vector is encrypted using CKKS, and floating-point similarities are computed in batch in the ciphertext domain of the requirements to obtain preliminary patent matches. Second, a δ safety band mechanism is designed to concentrate the samples that require exact review in the truly uncertain neighborhood, together with a secure-domain switching mechanism that converts ciphertexts from the CKKS approximate domain to the BFV fixed-point domain under minimal leakage. Finally, similarity computation over ciphertext is performed in BFV on these critical samples to achieve accurate patent review. Meanwhile, lightweight auditing is implemented on Hyperledger Fabric using commitment/hash techniques, enabling recomputation and accountability without exposing any plaintext. Based on modeled simulations and comparative experiments, the proposed method is evaluated against pure-CKKS and pure-BFV schemes. 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