A Physical Layer Analysis of Entropy in Delay-Based PUFs Implemented on FPGAs
<|reference_start|>A Physical Layer Analysis of Entropy in Delay-Based PUFs Implemented on FPGAs: Physical Unclonable Functions (PUFs) leverage signal variations that occur within the device as a source of entropy. On-chip instrumentation is utilized by some PUF architectures to measure and digitize these variations, which are then processed into bitstrings and secret keys for use in security functions such as authentication and encryption. In many cases, the variations in the measured signals are introduced by a sequence of components in the circuit structure defined by the PUF architecture. In particular, the Hardware-Embedded deLay PUF (HELP) measures delay variations that occur in combinational logic paths on Field Programmable Gate Arrays (FPGAs), which are composed of a set of interconnecting wires (nodes) and look-up tables (LUTs). Previous investigations of variations in these path delays show that it is possible to derive high quality bitstrings, i.e., those which exhibit high levels of uniqueness and randomness across the device population. However, the underlying source and level of variations associated with the constituent components of the paths remain unknown. In this paper, we apply statistical averaging and differencing techniques to derive estimates for the delay variation associated with an FPGA's basic components, namely LUTs and nodes, as a means of fully characterizing the PUF's source of entropy. The analysis is carried out on a set of 50,015 path delay measurements collected from a set of 20 Xilinx Zynq 7020 SoC-class FPGAs, on which 25 identical instances of a functional unit are instantiated, for a total of 500 instances.<|reference_end|>
@article{plusquellic2024a,
    title={A Physical Layer Analysis of Entropy in Delay-Based PUFs Implemented on
  FPGAs},
    author={Jim Plusquellic and Jennifer Howard and Ross MacKinnon and Kristianna
  Hoffman and Eirini Eleni Tsiropoulou and Calvin Chan},
    journal={arXiv preprint arXiv:2409.00825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00825},
    primaryClass={cs.CR}
}