Group Membership Verification with Privacy: Sparse or Dense?
This work addresses privacy-preserving biometric verification for groups, but it is incremental as it builds on existing quantization and aggregation mechanisms to analyze sparsity effects.
The paper tackles the problem of group membership verification with privacy, where a biometric trait is checked against a group without revealing identities, by analyzing the impact of sparsity in embeddings on security, compactness, and verification performance. It shows that a dense solution is more competitive unless queries are almost noiseless, bridging the gap towards a Bloom filter robust to noisy queries.
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 queries 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 model 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.