Improved Homomorphically Encrypted Biometric Identification Using Coefficient Packing

Efficient large-scale biometric identification is a challenging open problem in biometrics today. Adding biometric information protection by cryptographic techniques increases the computational workload even further. Therefore, this paper proposes an efficient and improved use of coefficient packing for homomorphically protected biometric templates, allowing for the evaluation of multiple biometric comparisons at the cost of one. In combination with feature dimensionality reduction, the proposed technique facilitates a quadratic computational workload reduction for biometric identification, while long-term protection of the sensitive biometric data is maintained throughout the system. In previous works on using coefficient packing, only a linear speed-up was reported. In an experimental evaluation on a public face database, efficient identification in the encrypted domain is achieved on off-the-shelf hardware with no loss in recognition performance. In particular, the proposed improved use of coefficient packing allows for a computational workload reduction down to 1.6% of a conventional homomorphically protected identification system without improved packing.