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A fair evaluation framework for comparing side-channel distinguishers

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)145-160
Number of pages16
JournalJournal of Cryptographic Engineering
Volume1
Issue number2
Early online date10 Aug 2011
DOIs
DateAccepted/In press - 9 Jul 2011
DateE-pub ahead of print - 10 Aug 2011
DatePublished (current) - Aug 2011

Abstract

The ability to make meaningful comparisons between side-channel distinguishers is important both to attackers seeking an optimal strategy and to designers wishing to secure a device against the strongest possible threat. The usual experimental approach requires the distinguishing vectors to be estimated: outcomes do not fully represent the inherent theoretic capabilities of distinguishers and do not provide a basis for conclusive, like-for-like comparisons. This is particularly problematic in the case of mutual information-based side channel analysis (MIA) which is notoriously sensitive to the choice of estimator. We propose an evaluation framework which captures those theoretic characteristics of attack distinguishers having the strongest bearing on an attacker’s general ability to estimate with practical success, thus enabling like-for-like comparisons between different distinguishers in various leakage scenarios. We apply our framework to an evaluation of MIA relative to its rather more well-established correlation-based predecessor and a proposed variant inspired by the Kolmogorov–Smirnov distance. Our analysis makes sense of the rift between the a priori reasoning in favour of MIA and the disappointing empirical findings of previous comparative studies and moreover reveals several unprecedented features of the attack distinguishers in terms of their sensitivity to noise. It also explores—to our knowledge, for the first time—theoretic properties of near-generic power models previously proposed (and experimentally verified) for use in attacks targeting injective functions.

    Research areas

  • Side-channel analysis, Mutual information, Kolmogorov–Smirnov, Differential power analysis

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