tl;dr: propose the usage of Gao et al.’s MI estimator for multivariate data (computable up to 30) and comparison against other notions of X-information (plus estimators) on different case studies.
Paper: Crypto 2024 or ePrint Version.
Aakash Chowdhury, Carlo Brunetta, Arnab Roy, Elisabeth Oswald
Side channel evaluations benefit from sound characterisations of adversarial leakage models, which are the determining factor for attack success. Two questions are of interest: can we define and estimate a quantity that captures the ideal adversary (who knows all the distributions that are involved in an attack), and can we define and estimate a quantity that captures a concrete adversary (represented by a given leakage model)?
Existing work has led to a proliferation of custom quantities to measure both types of adversaries, which can be data intensive to estimate in the ideal case, even for discrete side channels and especially when the number of dimensions in the side channel traces grows.
In this paper, we show how to define the mutual information between carefully chosen variables of interest and how to instantiate a recently suggested mutual information estimator for practical estimation. We apply our results to real-world data sets and are the first to provide a mutual information-based characterisation of ideal and concrete adversaries utilising up to 30 data points.