Abstract
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.
Highlights
The number of independent sources of information used to distinguish a system is a measure of its complexity, and relates to the amount of effort required to copy or clone it
Our results show that the processes proposed within Hardware-Embedded Delay PUF (HELP) to generate bitstrings add significantly to their Entropy, and show that classical re-use of physical unclonable function (PUF) components, e.g., path delays, does not result in large Entropy losses commonly reported for other PUF architectures
PUFs are widely recognized as next-generation security and trust primitives that are ideally suited for authentication in industrial, automotive, consumer and military Internet of Things (IoT)-based systems, and for dealing with many of the challenges related to counterfeits in the supply chain
Summary
The number of independent sources of information used to distinguish a system is a measure of its complexity, and relates to the amount of effort required to copy or clone it. The relationship between complexity and effort can be exponential, for systems designed to conceal or mask the information and only provide controlled access to it. The information embedded in a PUF is random, enabling it to serve hardware security and trust roles related to key generation, key management, tamper detection and authentication [1]. PUFs represent an alternative to storing keys in non-volatile-memory (NVM), thereby reducing cost and hardening the embedding system against key-extraction-based attacks. PUFs are widely recognized as next-generation security and trust primitives that are ideally suited for authentication in industrial, automotive, consumer and military IoT-based systems, and for dealing with many of the challenges related to counterfeits in the supply chain
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