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Modern biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions&#x2019; efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector&#x2019;s dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table lookups and summations only. We integrate the table lookup with HE and introduce pseudo-random permutations to enable cheap plaintext slot selection, which significantly saves the recognition runtime and brings a positive impact on the recognition performance. We then assess their runtime efficiency under encryption and record runtimes between 16.74ms and 49.84ms for both the cleartext and encrypted decision modes over the three security levels, demonstrating their enhanced speed for a compact encrypted reference template reduced to one ciphertext.

Improved Multiplication-Free Biometric Recognition Under Encryption