A Novel Feature Selection Evolutionary Computation Framework for Privacy Preservation

A Novel Feature Selection Evolutionary Computation Framework for Privacy Preservation | 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 A Novel Feature Selection Evolutionary Computation Framework for Privacy Preservation Duan-Ting Duan, Bing Sun, Jian-Yu Li, Qiang Yang, Xiao-Fang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4472728/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 application of feature selection to identify important features in data processing is critical for enhancing the efficiency of learning models, which not only reduces the training duration of the models but also enhances their interpretability. Evolutionary Computation (EC) is one of the most powerful and efficient feature selection methods, capable of identifying representative subsets of features through evolutionary processes in a reasonable computational time. However, with the increasing social and legal focus on privacy, the requirement to perform effective data analysis without revealing sensitive personal information has become a growing concern, posing new challenges to existing feature selection methods. To efficiently select features from privacy-sensitive data, this paper introduces a novel feature selection framework for EC to balance data privacy and computational efficiency. Specifically, a privacy-preserving framework is proposed that employs a rank-based cryptographic function to return encrypted fitness values rather than specific values, thereby ensuring privacy by preventing access to actual data information. Then, an elite population learning strategy is introduced to speed up EC algorithm convergence and prevent trapping in local optima. Finally, by integrating the privacy-preserving framework with enhanced EC algorithms, this paper introduces new methods for feature selection in privacy-sensitive data, and experiments on public datasets demonstrate that our algorithms not only preserve privacy but also enhance feature selection efficiency. Data Science Evolutionary Computation Privacy Preservation Feature Selection Differential Evolution Particle Swarm Optimization. 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. 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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-4472728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323339639,"identity":"daf8525d-db52-4da1-9c0f-e8ac0be0007d","order_by":0,"name":"Duan-Ting Duan","email":"","orcid":"","institution":"Communication University of China","correspondingAuthor":false,"prefix":"","firstName":"Duan-Ting","middleName":"","lastName":"Duan","suffix":""},{"id":323339640,"identity":"1bb49811-6996-44d0-8135-2e3ecb7cb3e8","order_by":1,"name":"Bing Sun","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Sun","suffix":""},{"id":323339641,"identity":"6a63aa56-8a37-4fe4-ad78-a141c4dfa3d2","order_by":2,"name":"Jian-Yu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACxmYwZcHDwMB84AOIycZOnBYJoBa2xBlgLczEWSYBxDyGYC0MhLQwtzM/e8zbJiHD33/mY8PHtm3yfMwMjB8+5uBzGJu5MVALj8SN3I2NM87cNmxjZmCWnLkNr1/MpEFaDCR4tz/mqbjNCNTCxsyLVwv7N4gW/jMPm3kMbtsToYUHagtDDpBdcTuRGC1lknPOgfySZgjyS3IbM2MzXr8Y9h/fJvGmzMaev//wQ2CI3bad39588MNHfFoaGBiYeNBsbsCtHgjkQUp+4FUyCkbBKBgFIx4AABePRnztrodXAAAAAElFTkSuQmCC","orcid":"","institution":"Nankai University","correspondingAuthor":true,"prefix":"","firstName":"Jian-Yu","middleName":"","lastName":"Li","suffix":""},{"id":323339642,"identity":"93c0b0e8-97ad-41f7-9459-1dcb119c54fd","order_by":3,"name":"Qiang Yang","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Yang","suffix":""},{"id":323339643,"identity":"ccfa1dd8-e520-43b9-b921-35161414c930","order_by":4,"name":"Xiao-Fang Liu","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Fang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-24 13:21:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4472728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4472728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60567990,"identity":"0f2cfd40-85d5-403d-bd29-23fcaebe8846","added_by":"auto","created_at":"2024-07-18 08:56:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806885,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript81523Wv10.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4472728/v1_covered_c50352f3-dd2c-4728-b4e2-8eb12567ae64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Feature Selection Evolutionary Computation Framework for Privacy Preservation","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":"Data Science, Evolutionary Computation, Privacy Preservation, Feature Selection, Differential Evolution, Particle Swarm Optimization.","lastPublishedDoi":"10.21203/rs.3.rs-4472728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4472728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The application of feature selection to identify important features in data processing is critical for enhancing the efficiency of learning models, which not only reduces the training duration of the models but also enhances their interpretability. 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