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

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...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 23; no. 11; p. 1538
Main Authors Tănăsescu, Andrei, Choudary, Marios O., Rioul, Olivier, Popescu, Pantelimon George
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 18.11.2021
MDPI
Subjects
Asymptotic properties
Computer Science
Cryptography and Security
Entropy
guessing entropy
Inequality
Information Theory
massey inequality
Mathematics
Probability
Probability distribution
Random variables
shannon entropy
side-channel attacks
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
PMCID: PMC8618297
ISSN:1099-4300
1099-4300
DOI:10.3390/e23111538