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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Autores principales: Tănăsescu, Andrei, Choudary, Marios O., Rioul, Olivier, Popescu, Pantelimon George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618297/
https://www.ncbi.nlm.nih.gov/pubmed/34828236
http://dx.doi.org/10.3390/e23111538
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author Tănăsescu, Andrei
Choudary, Marios O.
Rioul, Olivier
Popescu, Pantelimon George
author_facet Tănăsescu, Andrei
Choudary, Marios O.
Rioul, Olivier
Popescu, Pantelimon George
author_sort Tănăsescu, Andrei
collection PubMed
description 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.
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spelling pubmed-86182972021-11-27 Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy Tănăsescu, Andrei Choudary, Marios O. Rioul, Olivier Popescu, Pantelimon George Entropy (Basel) Article 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. MDPI 2021-11-18 /pmc/articles/PMC8618297/ /pubmed/34828236 http://dx.doi.org/10.3390/e23111538 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tănăsescu, Andrei
Choudary, Marios O.
Rioul, Olivier
Popescu, Pantelimon George
Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title_full Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title_fullStr Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title_full_unstemmed Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title_short Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
title_sort tight and scalable side-channel attack evaluations through asymptotically optimal massey-like inequalities on guessing entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8618297/
https://www.ncbi.nlm.nih.gov/pubmed/34828236
http://dx.doi.org/10.3390/e23111538
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