Machine Learning for Privacy Threat Classification: A Systematic Review

Machine Learning for Privacy Threat Classification: A Systematic Review | 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 Machine Learning for Privacy Threat Classification: A Systematic Review L.D.C.S. Subhashini, Yuefeng Li, Xiaohui Tao, Jianming Yong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6934585/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 Privacy threat classification plays a vital role in protecting sensitive information in today’s digital landscape, especially across domains such as the Internet of Things (IoT), smart devices, and cloud-based services. As traditional rule-based security mechanisms delay processing the limitations of under increasing data volume and threats, machine learning (ML) techniques have emerged as promising solutions. Despite significant advancements, these ML driven systems face challenges including data heterogeneity, sparsity, unlabeled data, class imbalance, evasion attacks, and concept drift which limit their performance in real-world applications. Existing surveys on this topic have largely focused on narrow domains such as IoT or federated learning, lacking limitations and omitting critical aspects such as how algorithms works, deployment scalability, and cross-domain applicability. In response, this systematic literature review aims to identify the available machine learning algorithms for privacy treat classification, limitations of the algorithms and evaluate current mitigation strategies. By addressing three core research questions, the study provides existing work, highlights unresolved challenges of ML algorithms, and proposes future research directions to mitigate these limitations Machine Learning Privacy Threats Classification 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. 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As traditional rule-based security mechanisms delay processing the limitations of under increasing data volume and threats, machine learning (ML) techniques have emerged as promising solutions. Despite significant advancements, these ML driven systems face challenges including data heterogeneity, sparsity, unlabeled data, class imbalance, evasion attacks, and concept drift which limit their performance in real-world applications. Existing surveys on this topic have largely focused on narrow domains such as IoT or federated learning, lacking limitations and omitting critical aspects such as how algorithms works, deployment scalability, and cross-domain applicability. In response, this systematic literature review aims to identify the available machine learning algorithms for privacy treat classification, limitations of the algorithms and evaluate current mitigation strategies. 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