Bias-Based Modeling and Entropy Analysis of PUFs
Physical Unclonable Functions (PUFs) are increasingly becoming a well-known security primitive for secure key storage and anti-counterfeiting. For both applications it is imperative that PUFs provide enough entropy. This paper is to propose a new model for binary-output PUFs such as SRAM, DFF, Latch and buskeeper PUFs, and a method to accurately estimate their entropy. In the authors' model the measurable property of a PUF is its set of cell biases. They determine an upper bound on the 'Extractable entropy', i.e. the number of key bits that can be robustly extracted, by calculating the mutual information between the bias measurements done at enrollment and reconstruction.