Welcome to the resource topic for 2021/681
Title:
Learnability of Multiplexer PUF and S_N-PUF : A Fourier-based Approach
Authors: Durba Chatterjee, Debdeep Mukhopadhyay, Aritra Hazra
Abstract:In this work, we prove that Multiplexer PUF~(MPUF) and S_N-PUF are learnable in the PAC model. First, we show that both the designs can be represented as a function of Linear Threshold Functions. We show that the noise sensitivity of (n,k)-MPUF and S_N-PUF can be bounded by O(2^{k} \sqrt{\epsilon}) and O(N\sqrt{\epsilon}) respectively. Finally, we show that as a result of bounded noise sensitivity, both the designs can be accurately approximated using low degree algorithm. Also, the number of labelled examples~(challenge-response pairs) required by the algorithm is polynomial in the input length and PAC model parameters.
ePrint: https://eprint.iacr.org/2021/681
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