通过对猜测熵的渐近最优类梅西不等式进行紧密且可扩展的边信道攻击评估。

Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy.

作者信息

Tănăsescu Andrei, Choudary Marios O, Rioul Olivier, Popescu Pantelimon George

机构信息

Department of Computer Science and Engineering, University Politehnica of Bucharest, Splaiul Independentei 313 (6), 060042 Bucharest, Romania.

LTCI, Télécom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France.

出版信息

Entropy (Basel). 2021 Nov 18;23(11):1538. doi: 10.3390/e23111538.

DOI:10.3390/e23111538

PMID:34828236

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8618297/

Abstract

The bounds presented at CHES 2017 based on Massey's guessing entropy represent the most scalable side-channel security evaluation method to date. In this paper, we present an improvement of this method, by determining the asymptotically optimal Massey-like inequality and then further refining it for finite support distributions. The impact of these results is highlighted for side-channel attack evaluations, demonstrating the improvements over the CHES 2017 bounds.

摘要

在2017年密码硬件与嵌入式系统会议（CHES 2017）上提出的基于梅西猜测熵的边界代表了迄今为止最具可扩展性的边信道安全评估方法。在本文中，我们对该方法进行了改进，确定了渐近最优的类梅西不等式，然后针对有限支持分布对其进行进一步优化。这些结果对边信道攻击评估的影响得到了突出体现，展示了相对于2017年密码硬件与嵌入式系统会议（CHES 2017）边界的改进。