Fast and Private 1-to-$N$ Face Identification Protocols

Nuttapong Attrapadung;Reo Eriguchi;Goichiro Hanaoka;Takahiro Matsuda;Naohisa Nishida;Tatsumi Oba;Jacob C. N. Schuldt;Koki Tejima;Tadanori Teruya;Yuji Unagami;Naoto Yanai
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引用次数: 0

Abstract

Face identification is a pivotal aspect of user authentication across diverse domains, spanning from smartphone security to access control in high-security environments. Privacy-preserving face identification aims to authenticate users using facial images while preserving their privacy. In this article, we focus on privacy-preserving protocols for one-to-many (1-to-$N$) face identification. Such protocols enable the authentication of individuals from a pool of registered users without disclosing their identity among the group. We present new protocols based on secret-sharing based secure multi-party computation (MPC). Our initial (warm-up) protocol directly applies MPC to the plain ArcFace framework, marking the first instance of such a face identification scheme without reliance on homomorphic encryption, a primary tool in previous works. Notably, this protocol exhibits efficiency for small-scale databases, requiring approximately 1 second for authentication among 1000 users. Building upon this foundation, our main contribution lies in our second protocol, designed to enhance scalability via a new approach to operations on large-scale databases. It significantly improves runtime performance compared to the state-of-the-art scheme of Bai et al., achieving approximately 2.31 times, 4.59 times, and 6.80 times faster authentication for registered user databases of sizes $N{=}10{,}000$ and $N{=}100{,}000$, and $N{=}1{,}000{,}000$, respectively. Notably, our protocol enables user authentication in about a second for the first time in the case of database with 30,000 users. While our second protocol offers fast authentication times, it does entail some leakage of intermediate values. Nevertheless, this leakage is minimal and far less than that of previous works that allow leakage. Through our contributions, we aim to propel the state-of-the-art in face identification protocols, striking a balance between the imperatives of efficiency and privacy in real-world applications.
快速和私人1到$N$人脸识别协议
从智能手机安全到高安全环境中的访问控制,面部识别是跨多个领域的用户认证的关键方面。保护隐私的人脸识别旨在使用人脸图像对用户进行身份验证,同时保护用户的隐私。在本文中,我们将重点讨论一对多(1-to-$N$)人脸识别的隐私保护协议。这种协议允许对注册用户池中的个人进行身份验证,而不会在组中泄露他们的身份。提出了基于秘密共享的安全多方计算(MPC)协议。我们的初始(预热)协议直接将MPC应用于普通ArcFace框架,标志着这种面部识别方案的第一个实例,而不依赖于同态加密,同态加密是以前工作中的主要工具。值得注意的是,该协议显示了小规模数据库的效率,在1000个用户中进行身份验证大约需要1秒。在此基础上,我们的主要贡献在于第二个协议,该协议旨在通过在大型数据库上操作的新方法来增强可伸缩性。与Bai等人最先进的方案相比,它显著提高了运行时性能,对于大小为$N{=}10{,}000$和$N{=}100{,}000$和$N{=}1{,}000{,}000$的注册用户数据库,分别实现了大约2.31倍,4.59倍和6.80倍的认证速度。值得注意的是,在拥有30,000个用户的数据库的情况下,我们的协议第一次在大约一秒钟内启用了用户身份验证。虽然我们的第二个协议提供了快速的身份验证时间,但它确实带来了一些中间值的泄漏。然而,这种泄漏是最小的,远远小于以前允许泄漏的工程。通过我们的贡献,我们的目标是推动最先进的人脸识别协议,在现实世界的应用中,在效率和隐私的必要性之间取得平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
12.60
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