Biometric data stored in automated recognition systems are at risk of attacks. This is particularly true for large-scale biometric identification systems, where the reference database is often accessed remotely. A popular approach for the protection of the stored templates is homomorphic encryption, which grants privacy protection while maintaining the biometric performance of the unprotected system. However, it introduces a significant computational overhead that can render identification transactions infeasible. To reduce this workload, biometric indexing in the encrypted domain has become a recent research interest. In this work, we show that in such schemes, auxiliary indexing data can leak additional privacy-sensitive information that violate standardized requirements for biometric template protection. In response to this leakage, we propose a novel framework HEBI that protects biometric indexing approaches at a post-quantum security level while requiring a computational effort of only 0.12 milliseconds per cluster.

Protecting minutiae-based fingerprint templates with fully homomorphic encryption has recently been recognised as a hard problem. In this work, we evaluate state-of-the-art fingerprint recognition based on minutiae templates using post-quantum secure fully homomorphic encryption that operates directly on floating point numbers, such that no simplification or quantisation of the comparison algorithm is necessary. In a practical evaluation on a publicly available dataset, we run a benchmark and provide directions for future work.

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. Adding to that, biometric data are sensitive data deserving adequate protection. As a solution, this work proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search (PEKS). For long-term protection of the biometric data, fully homomorphic encryption is applied for template protection. As all applied cryptographic schemes are lattice-based, they also offer post-quantum security. Throughout the system, the recognition accuracy of the unprotected system is preserved. In an evaluation on a public face database, the computational workload of an identification search in the encrypted domain is reduced down to 8.4% compared to an exhaustive search, achieving identification on 1062 subjects in 210 milliseconds. Based on these results, an identification search on 1 million subjects can be estimated at under 3 minutes using off-the-shelf hardware.