Debiasing Method for Efficient Ternary Fuzzy Extractors and Ternary Physically Unclonable Functions
Kohei Kazumori, Rei Ueno, Naofumi Homma · 2020
In this paper, we propose a debiasing method for efficiently extracting unbiased ternary strings from biased ternary physically unclonable functions (PUFs). The conventional debiasing method applicable to ternary PUFs extracts unbiased strings from ternary PUFs by discarding the response of certain cells according to the ternary-extended von Neumann corrector (VNC). The proposed method probabilistically extracts the information, which is discarded in the conventional method, as the third value based on a rejection sampling. To demonstrate the effectiveness and efficiency of the proposed method, we evaluate the PUF sizes required for reliable 128-bit cryptographic key generation from PUFs with varying biases and error rates. The results show that the proposed method can reduce the PUF size by 63%, compared with the conventional method.