Joint Learning of Assignment and Representation for Biometric Group Membership

Abstract

This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments show the trade-off between security/privacy and verification/identification performances.

Privacy-Preserving near Neighbor Search via Sparse Coding with Ambiguation

“…The term WX−A 2 F is a sparsification error, which represents the deviation of the transformed data from the exact sparse representation in the transformed domain. We refer the reader to [23]- [25] for applications in group membership verification. Our algorithm for solving (1) alternates between a 0 -"norm"-based sparse coding step, and a non-convex transform update step [26].…”