Privacy-preserving biometric person identification

In this paper we investigate biometric person identification. We model this process of person identification as multiple hypothesis testing and consider performance measures that can be attained in such a protocol in terms of exponents of average error probability. A special attention is paid to the privacy related issues. In particular, we consider performance/privacy trade-off due to the indirect identification that is based on data projection of an original data to a secret subspace. Finally, we approximate the obtained performance limits using properties of random projections. Finally, experimental simulations are used to exemplify our findings.