  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.
