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.

在密码学领域，在生物识别数据中实施纠错具有许多好处，包括安全数据存储和密钥派生。基于深度学习的解码器已成为在解码嘈杂的生物识别数据时改进纠错的催化剂。尽管这些解码器表现出逼近精确解的能力，但我们通过最小熵分析揭示了它们安全保证的潜在不足。这种限制限制了它们在安全生物识别环境中的适用性，因为其非线性神经网络架构的固有复杂性对精确建模解决方案分布构成了挑战。为了解决这一限制，我们引入了 U-Sketch，这是一种生物识别纠错的通用方法，它将任意输入的随机生物识别源分布转换为独立且同分布 （i.i.d.） 的数据，同时保持转换后数据的成对距离。这种方法确保了解码器内部的可解释性，促进了透明的熵分析和经证实的安全声明。此外，U-Sketch 采用最大似然解码，提供最佳容错和精确的安全保证。