{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/secure-face-matching-using-fully-homomorphic","title":"Secure Face Matching Using Fully Homomorphic Encryption","arxiv_id":"1805.00577","date":"2018-05-01","proceeding":null,"authors":["Vishnu Naresh Boddeti"],"abstract":"Face recognition technology has demonstrated tremendous progress over the\npast few years, primarily due to advances in representation learning. As we\nwitness the widespread adoption of these systems, it is imperative to consider\nthe security of face representations. In this paper, we explore the\npracticality of using a fully homomorphic encryption based framework to secure\na database of face templates. This framework is designed to preserve the\nprivacy of users and prevent information leakage from the templates, while\nmaintaining their utility through template matching directly in the encrypted\ndomain. Additionally, we also explore a batching and dimensionality reduction\nscheme to trade-off face matching accuracy and computational complexity.\nExperiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that\nsecure face matching can be practically feasible (16 KB template size and 0.01\nsec per match pair for 512-dimensional features from SphereFace) while\nexhibiting minimal loss in matching performance.","url_abs":"http://arxiv.org/abs/1805.00577v2","url_pdf":"http://arxiv.org/pdf/1805.00577v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"secure-face-matching-using-fully-homomorphic","repo_url":"https://github.com/human-analysis/secure-face-matching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"template-matching","task_name":"Template Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.00577","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}