Rethinking Contemporary Deep Learning Techniques for Error Correction in Biometric Data

Abstract

In the realm of cryptography, the implementation of error correction in biometric data offers many benefits, including secure data storage and key derivation. Deep learning-based decoders have emerged as a catalyst for improved error correction when decoding noisy biometric data. Although these decoders exhibit competence in approximating precise solutions, we expose the potential inadequacy of their security assurances through a minimum entropy analysis. This limitation curtails their applicability in secure biometric contexts, as the inherent complexities of their non-linear neural network architectures pose challenges in modeling the solution distribution precisely. To address this limitation, we introduce U-Sketch, a universal approach for error correction in biometrics, which converts arbitrary input random biometric source distributions into independent and identically distributed (i.i.d.) data while maintaining the pairwise distance of the data post-transformation. This method ensures interpretability within the decoder, facilitating transparent entropy analysis and a substantiated security claim. Moreover, U-Sketch employs Maximum Likelihood Decoding, which provides optimal error tolerance and a precise security guarantee.