Privacy-Preserving Preselection for Protected Biometric Identification Using Public-Key Encryption With Keyword Search

Abstract

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. In addition, biometric data are sensitive data deserving adequate protection. As a solution, this article proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search. For the long-term protection of the biometric data, fully homomorphic encryption is applied for template protection. As all the 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 ms. Based on these results, an identification search on 1 million subjects can be estimated at under 3 min using off-the-shelf hardware.