标题： IDFace：用于高效安全识别的人脸模板保护 显示英文标题

标题： IDFace: Face Template Protection for Efficient and Secure Identification

摘要： 随着人脸识别系统（FRS）的广泛应用，用户隐私变得越来越重要。 FRS中的一个关键隐私问题是保护用户的面部模板，因为可以从模板中恢复用户的面部图像特征。 尽管近年来诸如同态加密（HE）等密码学工具的进步为安全FRS提供了机会， 但HE无法以高效的方式直接与FRS进行即插即用的结合。 特别是，虽然HE在功能上可以完成任意程序，但它基本上是为对预定形状的加密数据（如多项式环）进行代数运算而设计的。 因此，HE与系统的非定制组合会导致性能非常低效，许多以前基于HE的人脸模板保护方法比没有保护的普通系统慢数百倍。 在本研究中，我们提出了IDFace，一种新的基于HE的安全且高效的带模板保护的人脸识别方法。 IDFace基于两种新技术，用于在使用角度度量的（同态加密的）生物特征数据库上进行高效搜索。 第一种技术是一种模板表示转换，可显著降低匹配测试的单位成本。 第二种是空间高效的编码，减少了加密算法带来的空间浪费，从而节省了对加密模板的操作次数。 通过实验，我们表明IDFace可以在126毫秒内从包含1M个加密模板的数据库中识别出人脸模板，与在明文上的识别相比仅增加了2倍的开销。 显示英文摘要

摘要： 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.