The magnitude of the information content associated with a particular implementation of a Physical Unclonable Function (PUF) is critically important for security and trust in emerging Internet of Things (IoT) applications. Authentication, in particular, requires the PUF to produce a very large number of challenge-response-pairs (CRPs) and, of even greater importance, requires the PUF to be resistant to adversarial attacks that attempt to model and clone the PUF (model-building attacks). Entropy is critically important to the model-building resistance of the PUF. A variety of metrics have been proposed for reporting Entropy, each measuring the randomness of information embedded within PUF-generated bitstrings. In this paper, we report the Entropy, MinEntropy, conditional MinEntropy, Interchip hamming distance and National Institute of Standards and Technology (NIST) statistical test results using bitstrings generated by a Hardware-Embedded Delay PUF called HELP. The bitstrings are generated from data collected in hardware experiments on 500 copies of HELP implemented on a set of Xilinx Zynq 7020 SoC Field Programmable Gate Arrays (FPGAs) subjected to industrial-level temperature and voltage conditions. Special test cases are constructed which purposely create worst case correlations for bitstring generation. Our results show that the processes proposed within HELP to generate bitstrings add significantly to their Entropy, and show that classical re-use of PUF components, e.g., path delays, does not result in large Entropy losses commonly reported for other PUF architectures.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited