On the optimality and practicability of mutual information analysis in some scenarios - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences Year : 2018

On the optimality and practicability of mutual information analysis in some scenarios

Abstract

The best possible side-channel attack maximizes the success rate and would correspond to a maximum likelihood (ML) distinguisher if the leakage probabilities were totally known or accurately estimated in a profiling phase. When profiling is unavailable, however, it is not clear whether Mutual Information Analysis (MIA), Correlation Power Analysis (CPA), or Linear Regression Analysis (LRA) would be the most successful in a given scenario. In this paper, we show that MIA coincides with the maximum likelihood expression when leakage probabilities are replaced by online estimated probabilities. Moreover, we show that the calculation of MIA is lighter that the computation of the maximum likelihood. We then exhibit two case-studies where MIA outperforms CPA. One case is when the leakage model is known but the noise is not Gaussian. The second case is when the leakage model is partially unknown and the noise is Gaussian. In the latter scenario MIA is more efficient than LRA of any order.
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Dates and versions

hal-03750327 , version 1 (12-08-2022)

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Èloi de Chèrisey, Sylvain Guilley, Annelie Heuser, Olivier Rioul. On the optimality and practicability of mutual information analysis in some scenarios. Cryptography and Communications - Discrete Structures, Boolean Functions and Sequences , 2018, 10 (1), pp.101-121. ⟨10.1007/s12095-017-0241-x⟩. ⟨hal-03750327⟩
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