On the Response Entropy of APUFs

TL;DR

This paper analyzes the response entropy of Arbiter PUFs by deriving closed-form expressions for the probability distribution of responses conditioned on known CRPs, enhancing understanding of their security and predictability.

Contribution

It provides the first closed-form derivation of conditional response probabilities and entropy for APUFs based on known CRPs, advancing the theoretical understanding of PUF security.

Findings

Derived closed-form probability mass functions for conditional responses.

Quantified response similarity and its impact on entropy.

Identified optimal challenge predictors based on known CRPs.

Abstract

A Physically Unclonable Function (PUF) is a hardware security primitive used for authentication and key generation. It takes an input bit-vector challenge and produces a single-bit response, resulting in a challenge-response pair (CRP). The truth table of all challenge-response pairs of each manufactured PUF should look different due to inherent manufacturing randomness, forming a digital fingerprint. A PUF's entropy (the entropy of all the responses, taken over the manufacturing randomness and uniformly selected challenges) has been studied before and is a challenging problem. Here we explore a related notion -- the response entropy, which is the entropy of an arbitrary response given knowledge of one (and two) other responses. This allows us to explore how knowledge of some CRP(s) impacts the ability to guess another response. The Arbiter PUF (APUF) is a well-known PUF architecture based on accumulated delay differences between two paths. In this paper, we obtain in closed form the probability mass function of any arbitrary response given knowledge of one or two other arbitrary CRPs for the APUF architecture. This allows us to obtain the conditional response entropy and then to define and obtain the size of the entropy bins (challenge sets with the same conditional response entropy) given knowledge of one or two CRPs. All of these results depend on the probability that two different challenge vectors yield the same response, termed the response similarity of those challenges. We obtain an explicit closed form expression for this. This probability depends on the statistical correlations induced by the PUF architecture together with the specific known and to-be-guessed challenges. As a by-product, we also obtain the optimal (minimizing probability of error) predictor of an unknown challenge given access to one (or two) challenges and the associated predictability.