StealthGuard: a new framework of privacy-preserving human action recognition

StealthGuard: a new framework of privacy-preserving human action recognition | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 January 2025 V1 Latest version Share on StealthGuard: a new framework of privacy-preserving human action recognition Authors : Gazi mohammad ismail 0009-0006-2373-4359 [email protected] , Xueping Zhang , Junxiang Yang , and Bin Li Authors Info & Affiliations https://doi.org/10.22541/au.173748268.82144779/v1 Published International Journal of Information and Computer Security Version of record Peer review timeline 344 views 175 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Privacy-preserving human action recognition is a crucial area of research, particularly in the context of video surveillance, assisted living systems, and healthcare applications. While human action recognition techniques offer significant benefits for automated video analysis, they also raise concerns about individual privacy when deployed in sensitive environments. This paper introduces, StealthGuard incorporates a temporal privacy-preserving component based on generative adversarial networks (GANs) to obfuscate sensor data, thereby preventing the identification of individual people or their activities. This approach utilises deep neural network, ensuring both accuracy in action recognition and real-time deployment feasibility. Through extensive experimental results, StealthGuard demonstrates its ability to achieve high levels of privacy protection while maintaining recognition accuracy making it a promising solution for applications where privacy is paramount. This paper also provides a related works in the field, highlighting approaches and techniques for privacy-preserving human action recognition. Supplementary Material File (2024_ijics-209944_atfpv.pdf) Download 1.84 MB Information & Authors Information Version history V1 Version 1 21 January 2025 Peer review timeline Published International Journal of Information and Computer Security Version of Record 1 Jan 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords editorial human activity recognition leadership computing Authors Affiliations Gazi mohammad ismail 0009-0006-2373-4359 [email protected] Computer Science and Technology, School of Information Science and Engineering, Henan University of Technology View all articles by this author Xueping Zhang Computer Science and Technology, School of Information Science and Engineering, Henan University of Technology View all articles by this author Junxiang Yang Computer Science and Technology, School of Information Science and Engineering, Henan University of Technology View all articles by this author Bin Li Computer Science and Technology, School of Information Science and Engineering, Henan University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 344 views 175 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Gazi mohammad ismail, Xueping Zhang, Junxiang Yang, et al. StealthGuard: a new framework of privacy-preserving human action recognition. Authorea . 21 January 2025. DOI: https://doi.org/10.22541/au.173748268.82144779/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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