Privacy Preserving Activity Recognition Framework for High Performance Smart Systems

Privacy Preserving Activity Recognition Framework for High Performance Smart Systems | 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 Privacy Preserving Activity Recognition Framework for High Performance Smart Systems Mohammed GH. I. AL Zamil, Samer M. Samarah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5059844/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 Remote recognition of human activities has revolutionized the healthcare industry by enabling smart monitoring of patients at their residents. However, such systems often expose sensitive data, making them vulnerable to breaches. Preserving data confidentiality is a problem that remains underexplored in smart activity recognition, despite its important in preserving patients’ privacy. This paper aims to develop an optimized feature augmentation technique using fractional derivatives to increase data uncertainty for improved confidentiality, while maintaining acceptable classification accuracy. A key challenge in this domain is the balancing of privacy and performance. To achieve this goal, the proposed framework utilizes multilayer perceptron neural networks that are used to embed multiple modalities of data and integrate them into a coherent structure. Validation was performed using five state-of-the-art classification techniques to measure the performance of the proposed framework in terms of classification accuracy and data confidentiality. The results elevate the potential of the proposed methodology to enable quality healthcare services in terms of confidentiality and performance. Feature Augmentation Fractional Derivatives Smart Healthcare Cyber threats data Privacy Data Confidentiality Activity Recognition Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 21 Oct, 2024 Reviews received at journal 29 Sep, 2024 Reviews received at journal 29 Sep, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers invited by journal 23 Sep, 2024 Editor assigned by journal 10 Sep, 2024 Submission checks completed at journal 09 Sep, 2024 First submitted to journal 09 Sep, 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. 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-5059844","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381149321,"identity":"b04f0a15-59fd-4964-8e56-3cfcee5f2f2e","order_by":0,"name":"Mohammed GH. I. 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