On the optimality and practicability of mutual information analysis in some scenarios
Résumé
The best possible side-channel attack maximizes the success rate and would correspond to a maximum likelihood distinguisher if the leak- age 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 prob- abilities.
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.
Domaines
Cryptographie et sécurité [cs.CR] Théorie de l'information et codage [math.IT] Mathématique discrète [cs.DM] Interface homme-machine [cs.HC] Traitement du signal et de l'image [eess.SP] Traitement du signal et de l'image [eess.SP] Statistiques [math.ST] Probabilités [math.PR] Analyse fonctionnelle [math.FA] Analyse classique [math.CA] Mathématiques générales [math.GM] Théorie de l'information [cs.IT]
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