Abstract
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally complete for arbitrary programs, it is basically designed for algebraic operations on encrypted data of predetermined shape, such as a polynomial ring. Thus, a non-tailored combination of HE and the system can yield very inefficient performance, and many previous HE-based face template protection methods are hundreds of times slower than plain systems without protection. In this study, we propose IDFace, a new HE-based secure and efficient face identification method with template protection. IDFace is designed on the basis of two novel techniques for efficient searching on a (homomorphically encrypted) biometric database with an angular metric. The first technique is a template representation transformation that sharply reduces the unit cost for the matching test. The second is a space-efficient encoding that reduces wasted space from the encryption algorithm, thus saving the number of operations on encrypted templates. Through experiments, we show that IDFace can identify a face template from among a database of 1M encrypted templates in 126ms, showing only 2X overhead compared to the identification over plaintexts.
Abstract (translated)
随着面部识别系统（FRS）的广泛应用，用户隐私变得越来越重要。在FRS中，一个关键的隐私问题是如何保护用户的面部模板，因为可以从该模板中恢复出用户面部图像的特征。尽管最近在密码学工具如同态加密（HE）方面取得了一些进展，为保障FRS的安全性提供了机会，但直接将HE应用于FRS并不能实现高效的一键式操作。特别是，虽然HE理论上可以用于任意程序的功能完成，并且主要是为了对预定形状（例如多项式环）的加密数据进行代数运算而设计，但在非定制组合中使用HE和系统时会显著降低效率，许多基于HE的方法在保护面部模板方面比没有保护的传统系统慢数百倍。 本文提出了一种新的、基于HE的安全高效的人脸识别方法——IDFace，并具有模板保护功能。该方法基于两种用于在生物特征数据库（同态加密）中以角度度量进行有效搜索的创新技术而设计。第一项技术是一种模板表示转换，它显著降低了匹配测试中的单次成本；第二项技术则是一种节省空间的编码方式，通过减少加密算法造成的浪费空间来降低对加密模板的操作次数。 实验表明，IDFace能够在126毫秒内从一个包含一百万个加密面部模板的数据库中识别出一个特定的面部模板，并且仅比处理明文数据时多花费两倍的时间。
URL
https://arxiv.org/abs/2507.12050
