Title: A novel computational model for SRAM PUF min-entropy estimation

Authors: Dongfang Li; Qiuyan Zhu; Hong Wang; Wenchao Liu; Zhihua Feng; Jianwei Zhang

Addresses: The Second Academy of China Aerospace Science and Industry Corporation, Institute 706, 100854, Haidian District, Beijing, China ' The Second Academy of China Aerospace Science and Industry Corporation, Institute 706, 100854, Haidian District, Beijing, China ' The Second Academy of China Aerospace Science and Industry Corporation, Institute 706, 100854, Haidian District, Beijing, China ' Hubei University, No. 28, Nanli Road, Hong-shan District, Wuchang, Wuhan, Hubei Province, China ' The Second Academy of China Aerospace Science and Industry Corporation, Institute 706, 100854, Haidian District, Beijing, China ' The Second Academy of China Aerospace Science and Industry Corporation, Institute 706, 100854, Haidian District, Beijing, China

Abstract: Min-entropy is the standard for quantisation of uncertainty of security key source under the worst case. It indicates the upper bound of length of security key that is able to be extracted from its source. The openly published min-entropy estimation methods are all based on experimental data or statistical tests to obtain the underlying probability distribution of the security key source, where huge number of samples are required and therefore are not feasible from engineering perspective. Aimed at computational complexity optimisation, this paper proposes a novel model for SRAM PUF min-entropy estimation based on the generic coupling relationship between entropy and average energy consumption of the SRAM cell. The model mainly investigates the way of min-entropy evaluation derived from the average energy consumption of memory cell during power-up stage via simulation. We apply the model to estimate the min-entropy of IS62WV51216BLL SRAM chip. The experimental results demonstrated that the accuracy of the proposed min-entropy estimation model is in parallel with that of conventional methods while its computational efficiency surpasses them to a large extent.

Keywords: min-entropy; SRAM; PUF; estimation model; entropy-energy coupling.

DOI: 10.1504/IJCSE.2019.100242

International Journal of Computational Science and Engineering, 2019 Vol.19 No.2, pp.215 - 222

Received: 02 May 2018
Accepted: 02 Aug 2018

Published online: 17 Jun 2019 *

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