Group Membership Verification with Privacy: Sparse or Dense?

Abstract

Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms: quantizing templates into discrete embeddings, and aggregating several templates into one group representation.However, this scheme has one drawback: the data structure representing the group has a limited size and cannot recognize noisy query when many templates are aggregated. Moreover, the sparsity of the embeddings seemingly plays a crucial role on the performance verification.This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances. This models bridges the gap towards a Bloom filter robust to noisy queries. It shows that a dense solution is more competitive unless the queries are almost noiseless.

Joint Learning of Assignment and Representation for Biometric Group Membership

“…Recently [5,6,7] proposed a framework based on aggregation and embedding of several biometric signatures into a unique vector representing the members of a group. It has been demonstrated that this allows a good assessment of the membership property at test time provided that the groups are small.…”