A Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine

A Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine | 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 Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine Chunlin Li, Min Wang, Ling Xiong, Xucheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8044369/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 In modern telemedicine and smart healthcare systems, secure retrieval of medical records is essential, as such data are highly sensitive and valuable for clinical decision-making. Existing privacy-preserving query frameworks protect user privacy but often incur high computational costs and limited efficiency when applied to large-scale datasets. This paper presents a lightweight privacy-preserving medical record search framework that integrates symmetric homomorphic encryption with a group-oriented query strategy, achieving both computational efficiency and strong privacy guarantees. Users generate encrypted query vectors under private parameters, ensuring that sensitive information remains concealed. The cloud server partitions the dataset into structured groups with predetermined identifiers, which narrows the search scope and reduces server-side computation. Encrypted queries are directly evaluated against grouped ciphertext records without exposing plaintext. Once relevant records are identified, the server derives a session key linked to the encrypted query and re-encrypts the results, enabling only legitimate users to verify and decrypt the outputs. The proposed framework enhances the timeliness of information acquisition under high-throughput conditions such as 5G, supporting rapid and large-scale data exchange. Security analysis confirms query confidentiality and resistance to unauthorized inference. Experimental evaluations demonstrate a improvement in query response times compared to state-of-the-art alternatives, alongside a substantial reduction in server-side processing load. These performance gains, coupled with compatibility for resource-constrained environments, position our framework as a scalable and efficient solution for large-scale remote e-healthcare systems. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Medical record search Telemedicine Privacy-preserving Symmetric homomorphic encryption 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. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8044369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":553725325,"identity":"9b211fd1-ec44-4cf1-b2c5-85ed95945f2a","order_by":0,"name":"Chunlin Li","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Li","suffix":""},{"id":553725329,"identity":"323cfeb5-8fce-45c6-ad3d-8924fa637ba0","order_by":1,"name":"Min Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYNACAyBmb2x8+IE0LTyHm40lSLNJIr1NgIco84+fPfzqRsGdfH7Jh20MEgx2croNhLScyUuzzjF4ZjlzdmLbgwKGZGOzAwS0mB3IMTPOMThsYHA7sd1AguFA4jaCWs6/gWixv3mwTYKHKC03cowfg22RYCRSi/2NN2bMIC0SZxKBgWxAhF8k+3OMP+f8OWzA33784cMPFXZyBLUAARtSBBoQVg4CzCQkk1EwCkbBKBiRAADoCEOLwe0BzgAAAABJRU5ErkJggg==","orcid":"","institution":"Xihua University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Wang","suffix":""},{"id":553725331,"identity":"c5b64eb4-0bae-43fa-b352-4fc65a67c064","order_by":2,"name":"Ling Xiong","email":"","orcid":"","institution":"Beijing Language and Culture University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Xiong","suffix":""},{"id":553725333,"identity":"3afc8921-8486-43a4-a023-04f24f2a7f98","order_by":3,"name":"Xucheng Zhang","email":"","orcid":"","institution":"Xihua University","correspondingAuthor":false,"prefix":"","firstName":"Xucheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-11-06 06:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8044369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8044369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97232959,"identity":"acece1ee-dbb0-4eb1-a00d-713c6606a7cb","added_by":"auto","created_at":"2025-12-02 09:50:33","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6101,"visible":true,"origin":"","legend":"","description":"","filename":"26479c0f43ec4967814b0a7d1e969922.json","url":"https://assets-eu.researchsquare.com/files/rs-8044369/v1/a95c0d1c41ff1c66b571918a.json"},{"id":104780303,"identity":"eb8c1d50-98c7-4f15-a228-c89ad457b16c","added_by":"auto","created_at":"2026-03-17 07:52:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2655902,"visible":true,"origin":"","legend":"","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8044369/v1_covered_172a297a-9541-4305-a5a6-ab91b9fc57b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine","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":"Medical record search, Telemedicine, Privacy-preserving, Symmetric homomorphic encryption","lastPublishedDoi":"10.21203/rs.3.rs-8044369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8044369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In modern telemedicine and smart healthcare systems, secure retrieval of medical records is essential, as such data are highly sensitive and valuable for clinical decision-making. Existing privacy-preserving query frameworks protect user privacy but often incur high computational costs and limited efficiency when applied to large-scale datasets. This paper presents a lightweight privacy-preserving medical record search framework that integrates symmetric homomorphic encryption with a group-oriented query strategy, achieving both computational efficiency and strong privacy guarantees. Users generate encrypted query vectors under private parameters, ensuring that sensitive information remains concealed. The cloud server partitions the dataset into structured groups with predetermined identifiers, which narrows the search scope and reduces server-side computation. Encrypted queries are directly evaluated against grouped ciphertext records without exposing plaintext. Once relevant records are identified, the server derives a session key linked to the encrypted query and re-encrypts the results, enabling only legitimate users to verify and decrypt the outputs. 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