A Secure Cloud-Based Framework for Privacy-Preserving Medical Pre-Diagnosis Using Encrypted Machine Learning

A Secure Cloud-Based Framework for Privacy-Preserving Medical Pre-Diagnosis Using Encrypted Machine 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 Method Article A Secure Cloud-Based Framework for Privacy-Preserving Medical Pre-Diagnosis Using Encrypted Machine Learning Viraj Gulhane, Sandeep Rode, Nikesh Gadare, Pankaj Gadge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9493732/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 rapid adoption of cloud computing in healthcare has improved data accessibility but introduced significant challenges in privacy preservation. This study presents a secure cloud-based framework for medical pre-diagnosis using encrypted machine learning techniques. The proposed approach applies matrix- based encryption and a privacy-preserving Mahalanobis distance mechanism to enable classification directly on protected data. A hybrid model combining K- Nearest Neighbor (KNN) with ensemble methods such as Random Forest and XGBoost is used to enhance predictive accuracy and robustness. To improve efficiency, a hierarchical indexing structure is incorporated for faster retrieval of encrypted records, while a trapdoor-based query mechanism ensures secure inter- action without revealing sensitive inputs. Experimental evaluation demonstrates that the framework achieves accuracy above 98% with low computational over- head. The results confirm that the proposed system effectively balances security, efficiency, and predictive performance, making it suitable for scalable and reliable cloud-based healthcare analytics. Biomedical Engineering Artificial Intelligence and Machine Learning Cloud Computing Encrypted Machine Learning KNN XGBoost SVM Privacy Preservation Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplimentryData1.png System Architecture 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-9493732","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":627611167,"identity":"5f5f86d8-4bd3-4a49-98c2-9e334fc3beef","order_by":0,"name":"Viraj 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