Group Testing for Identification with Privacy

This paper describes an approach where group testing helps in enforcing security and privacy in identification. We detail a particular scheme based on embedding and group testing. We add a second layer of defense, group vectors, where each group vector represents a set of dataset vectors. Whereas the selected embedding poorly protects the data when used alone, the group testing approach makes it much harder to reconstruct the data when combined with the embedding. Even when curious server and user collude to disclose the secret parameters, they cannot accurately recover the data. Another byproduct of our approach is that it reduces the complexity of the search and the required storage space. We show the interest of our work in a benchmark biometrics dataset, where we verify our theoretical analysis with real data.