A noise bifurcation architecture for linear additive physical functions

Abstract

Physical Unclonable Functions (PUFs) allow a silicon device to be authenticated based on its manufacturing variations using challenge/response evaluations. Popular realizations use linear additive functions as building blocks. Security is scaled up using non-linear mixing (e.g., adding XORs). Because the responses are physically derived and thus noisy, the resulting explosion in noise impacts both the adversary (which is desirable) as well as the verifier (which is undesirable). We present the first architecture for linear additive physical functions where the noise seen by the adversary and the noise seen by the verifier are bifurcated by using a randomized decimation technique and a novel response recovery method at an authentication verification server. We allow the adversary's noise ηa → 0.50 while keeping the verifier's noise ηv constant, using a parameter-based authentication modality that does not require explicit challenge/response pair storage at the server. We present supporting data using 28nm FPGA PUF noise results as well as machine learning attack results. We demonstrate that our architecture can also withstand recent side-channel attacks that filter the noise (to clean up training challenge/response labels) prior to machine learning.