A new maskless debiasing method for lightweight physical unclonable functions

An ideal Physical Unclonable Function produces a string of static random bits. Noise causes these bits to be unstable over subsequent readings and biases cause these bits to have a tendency towards a fixed value. Although the debiasing of random strings is a well-studied problem, the combined problem of noise and bias is unique to PUF design. This paper proposes a new lightweight noise-aware debiasing method superior to earlier techniques. The method is based on identifying an m-to-l encoding that compresses m-bit noisy and biased PUF outputs into l-bit strings which have a reduced combined effect of bias and noise. We describe a methodology for deriving an efficient encoding based on the bias and noise level of the input string. Notably, the method does not require intermediate storage or transmission of PUF-specific mask (debiasing helper) data for reconstruction. We test our method on PUFs with a range of bias and noise levels, and demonstrate its advantages over two debiasing approaches published at CHES 2015 which are based on XOR operation and Von Neumann corrector. The results quantify that the proposed method can achieve up to 76% reduction over the previous method in the number of PUF bits required to establish an authentication system with an error rate of one part in a million and a security level of 80-bits.