Cryptographie key generation from PUF data using efficient fuzzy extractors

Physical unclonable functions (PUFs) and biometrics are inherently noisy. When used in practice as cryptographic key generators, they need to be combined with an extraction technique to derive reliable bit strings (i.e., cryptographic key). An approach based on an error correcting code was proposed by Dodis et al. and is known as a fuzzy extractor. However, this method appears to be difficult for non-specialists to implement. In our recent study, we reported the results of some example implementations using PUF data and presented a detailed implementation diagram. In this paper, we describe a more efficient implementation method by replacing the hash function output with the syndrome from the BCH code. The experimental results show that the Hamming distance between two keys vary according to the key size and information-theoretic security has been achieved.