Tackling Biased PUFs Through Biased Masking: A Debiasing Method for Efficient Fuzzy Extractor

This paper presents an efficient fuzzy extractor (FE) design for biased physically unclonable functions (PUFs). To remove entropy leak from helper data in an efficient manner, we propose a new debiasing method, namely biased masking (BM). The proposed scheme removes the entropy leak by applying artificial noise (i.e., biased mask) such that the resulting response is uniform, and the added noise is removed by ECC decoding at the reconstruction as well as PUF noise. In addition, BM-based debiasing can be easily implemented with only additional random number generator and bit-parallel AND or OR operation in an enrollment server. Client devices with PUF, which are sometimes resource-constrained, require no additional operation. Furthermore, we show that the BM-based FE is reusable as well as the conventional code-offset FE. We evaluate the efficiency and effectiveness of the BM-based FE compared with the conventional debiasing-based FEs. Consequently, we confirm that the BM-based FE can achieve approximately 20 percent lower PUF size for nonnegligible biases (e.g., 60 percent) by just increasing the length of repetition code, which indicates that the BM-based FE is suitable for resource-constrained devices in terms of hardware cost for implementing PUF and computational cost at the reconstruction.