Abstract
When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two mechanisms: quantizing biometric templates into discrete embeddings and aggregating several templates into one group representation. This paper significantly improves that contribution because it jointly learns how to embed and aggregate instead of imposing fixed and hard coded rules. This is demonstrated by exposing the mathematical underpinnings of the learning stage before showing the improvements through an extensive series of experiments targeting face recognition. Overall, experiments show that learning yields an excellent trade-off between security /privacy and verification /identification performances.
Abstract (translated)
在保护隐私时，组成员身份验证会检查生物特征是否与组中的一个成员对应，而不会显示该成员的身份。类似地，团体成员身份标识说明个人属于哪个团体，而不知道他/她的身份。最近的一项贡献通过联合使用两种机制为组成员协议提供了隐私和安全性：将生物特征模板量化为离散嵌入，并将多个模板聚合为一个组表示。本文显著提高了这一贡献，因为它共同学习如何嵌入和聚合，而不是强制实施固定的和硬编码的规则。这一点可以通过暴露学习阶段的数学基础来证明，然后通过一系列针对人脸识别的广泛实验来展示这些改进。总的来说，实验表明，学习在安全性/隐私性和验证/识别性能之间产生了良好的权衡。
URL
https://arxiv.org/abs/1904.10327
