Breaking Free From Entropy’s Shackles: Cosine Distance-Sensitive Error Correction for Reliable Biometric Cryptography

Biometric cryptosystems present a promising avenue for secure authentication; however, the efficiency and security of such systems can be hindered by errors in biometric data. To address this challenge, existing systems employ error-correction codes, but often fail to consider the distribution of biometric sources, potentially leading to an underestimation of the system’s security. In response to this issue, we propose a novel algorithm pair, designated as
$\mathsf {ENCODE}$
and
$\mathsf {DECODE}$
, which facilitates direct codeword generation from biometric samples. Our approach accounts for the distribution of biometric sources, thereby providing a more accurate estimation of system security compared to traditional methods. Our proposed algorithm pair generates codewords that maintain interpretability and are sensitive to the cosine distance between original biometric samples. This similarity metric is particularly well-suited for high-dimensional data analysis and enables a precise assessment of system performance. We have rigorously established the correctness of our algorithm pair, and empirical results illustrate its efficacy in tolerating distance between codewords while preserving accuracy in cosine distance-sensitive contexts. This approach has the potential to significantly improve the efficiency and security of biometric cryptosystems, rendering them more appropriate for daily cryptographic applications.