HEBI: Homomorphically Encrypted Biometric Indexing
Pia Bauspieß, Marcel Grimmer, Cecilie Fougner, Damien Le Vasseur, Thomas Thaulow Stöcklin, Christian Rathgeb, Jascha Kolberg, Anamaria Costache, Christoph Busch · 2023
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.