Practical Biometric Search Under Encryption: Meeting the NIST Runtime Requirement Without Loss of Accuracy

Biometric search consists of comparing a biometric probe with biometric references in a database to find identities that match the probe. In addition to being resource-intensive, biometric search can lead to privacy violations when biometric data is exposed. To protect the sensitive biometric data, state-of-the-art search solutions are based on fully homomorphic encryption (FHE). However, they either reduce search accuracy to gain efficiency or lack efficiency and fail to meet the 10s NIST-FRVT requirement for a one-in-a-million search. In this paper, we present a fast and accurate biometric search solution under encryption for biometric data represented as vectors, a commonly used compact representation for compressing high-dimensional data. Our search solution is exhaustive and does not require any pre-selection (e.g., indexing) or dimensionality reduction techniques, which can degrade search accuracy. Also, it is parallelizable and supports dynamic databases. Our efficiency gain stems from our chunking and vertical organization of the encrypted reference database. This enables us to optimally leverage the single-instruction multiple-data (SIMD) property of FHE schemes. For a database of one million encrypted references represented by 512-dimensional vectors and a security level of 128 bits, our solution runs a fully encrypted exhaustive search in 9.23s measured on a 64-bit computer Intel Xeon Platinum 8358 with 64 CPUs and 64GB RAM. Hence, our solution is the first to satisfy the 10s NIST-FRVT limit under encryption, outperforming the state-of-the-art solutions by more than two orders of magnitude (at least 246 times faster) while achieving an accuracy improvement of at least 4.6% FNIR for a fixed FPIR at 0.1%.