A Privacy-Preserving Data Augmentation Approach for Credit Card Fraud Detection

A Privacy-Preserving Data Augmentation Approach for Credit Card Fraud Detection | 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 A Privacy-Preserving Data Augmentation Approach for Credit Card Fraud Detection Jun Wen, Xiusheng Li, Liu Long, Xiaoli Li, Hang Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4921709/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Credit card fraud costs credit card companies billions of dollars annually. Privacy concerns prevent most banks from sharing transaction data. Federated Learning enables multiple companies to train a unified model while preserving the privacy of sensitive data. The scarcity of credit card fraud samples has posed a challenge for Federated Learning (FL). To address this challenge, this paper proposes the Federated Learning Synthetic Minority Oversampling Technique (FL-SMOTE). It employs both partially and fully homomorphic encryption schemes, leveraging their strengths to enhance performance and security. Partially homomorphic encryption is used for Euclidean distance summation, ciphertext comparison, and privacy-preserving ranking of Euclidean distances. The Cheon-Kim-Kim-Song (CKKS) method is used to generate synthetic minority class samples and perform secure aggregation in federated learning. CKKS approximation methods are used to balance computational complexity and accuracy. The logistic regression model is adapted to demonstrate how the CKKS scheme can be seamlessly integrated into the training process. A quadratic term is added to the client’s loss function to regularize the discrepancy between the local model and the accelerated global model. Finally, the proposed design is evaluated on two publicly available datasets. The experiments demonstrate that the FL-SMOTE algorithm enhances training results and achieves the oversampling objective. Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Federated Learning (FL) Credit card fraud detection Synthetic Minority Class Oversampling Techniques (SMOTE) Homomorphic encryption Data imbalance Cheon-Kim-Kim-Song (CKKS) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Oct, 2024 Reviews received at journal 05 Oct, 2024 Reviews received at journal 15 Sep, 2024 Reviewers agreed at journal 13 Sep, 2024 Reviewers agreed at journal 05 Sep, 2024 Reviewers invited by journal 03 Sep, 2024 Editor assigned by journal 03 Sep, 2024 Editor invited by journal 03 Sep, 2024 Submission checks completed at journal 03 Sep, 2024 First submitted to journal 15 Aug, 2024 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-4921709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":357976638,"identity":"8f47ad4e-185c-4491-8ad1-a749ae7201cf","order_by":0,"name":"Jun Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJACAwYGGxibmWgtaTDVRGoBgsMkaDE43nugmHfH+cR+/vMHPzBUWCc2sJ89gF/LmXMJxrxnbifOnJHMLMFwJj2xgScvAa8Wsxs5Bsa8bbcTN9xgZmNgbDuc2CDBY4Bfy/03IC3nEvefPwzU8o8YLTd4QFoOJG5gSAZqaSBCi/2ZHAPDuW3JxjNuJBtLJBxLN27jycGvRbL9jJnB2zY72f7+gw8/fKixlu1nP4NfCxCwIVQkgLiE1AMB8wMiFI2CUTAKRsFIBgDy1kKLDF386gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wen","suffix":""},{"id":357976640,"identity":"cb096c01-f903-423d-a3b2-dcee0af52c6c","order_by":1,"name":"Xiusheng Li","email":"","orcid":"","institution":"xinwang bank","correspondingAuthor":false,"prefix":"","firstName":"Xiusheng","middleName":"","lastName":"Li","suffix":""},{"id":357976642,"identity":"3a2e3b25-2ee6-4835-8893-ab963f682699","order_by":2,"name":"Liu Long","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Long","suffix":""},{"id":357976644,"identity":"a4fd82f5-65ed-4cd9-b45c-876f32dd9277","order_by":3,"name":"Xiaoli Li","email":"","orcid":"","institution":"HuBei University of Arts and Science","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Li","suffix":""},{"id":357976646,"identity":"c95b883a-81b0-4348-aead-6a58baf63899","order_by":4,"name":"Hang Mao","email":"","orcid":"","institution":"xinwang bank","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2024-08-16 01:02:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4921709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4921709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65920681,"identity":"a951b912-9391-4ad3-af93-18dc8b9e61e7","added_by":"auto","created_at":"2024-10-04 11:47:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":571309,"visible":true,"origin":"","legend":"","description":"","filename":"smote5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4921709/v1_covered_0f0cd50a-c14d-4354-9e74-27c459228bad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Privacy-Preserving Data Augmentation Approach for Credit Card Fraud Detection","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":"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":"Federated Learning (FL), Credit card fraud detection, Synthetic Minority Class Oversampling Techniques (SMOTE), Homomorphic encryption, Data imbalance, Cheon-Kim-Kim-Song (CKKS)","lastPublishedDoi":"10.21203/rs.3.rs-4921709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4921709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCredit card fraud costs credit card companies billions of dollars annually. Privacy concerns prevent most banks from sharing transaction data. Federated Learning enables multiple companies to train a unified model while preserving the privacy of sensitive data. The scarcity of credit card fraud samples has posed a challenge for Federated Learning (FL). To address this challenge, this paper proposes the Federated Learning Synthetic Minority Oversampling Technique (FL-SMOTE). It employs both partially and fully homomorphic encryption schemes, leveraging their strengths to enhance performance and security. Partially homomorphic encryption is used for Euclidean distance summation, ciphertext comparison, and privacy-preserving ranking of Euclidean distances. The Cheon-Kim-Kim-Song (CKKS) method is used to generate synthetic minority class samples and perform secure aggregation in federated learning. CKKS approximation methods are used to balance computational complexity and accuracy. 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