Grote: Group Testing for Privacy-Preserving Face Identification

Alberto Ibarrondo, H. Chabanne, V. Despiegel, Melek Önen
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引用次数: 1

Abstract

This paper proposes a novel method to perform privacy-preserving face identification based on the notion of group testing, and applies it to a solution using the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Securely computing the closest reference template to a given live template requires K comparisons, as many as there are identities in a biometric database. Our solution, named Grote, replaces element-wise testing by group testing to drastically reduce the number of such costly, non-linear operations in the encrypted domain from K to up to 2\sqrtK . More specifically, we approximate the max of the coordinates of a large vector by raising to the α-th power and cumulative sum in a 2D layout, incurring a small impact in the accuracy of the system while greatly speeding up its execution. We implement Grote and evaluate its performance.
Grote:保护隐私的人脸识别组测试
提出了一种基于群测试的人脸识别方法,并将其应用于CKKS (Cheon-Kim-Kim-Song)同态加密方案。安全地计算与给定活动模板最接近的参考模板需要K次比较,与生物识别数据库中的身份一样多。我们的解决方案,名为Grote,用组测试取代了元素测试,从而大大减少了加密域中这种昂贵的非线性操作的数量,从K到最多2\sqrtK。更具体地说,我们通过提高到二维布局中的α-次幂和累积和来近似大矢量坐标的最大值,这对系统的精度产生了很小的影响,同时大大加快了其执行速度。我们实现Grote并评估其性能。
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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