Paper 2020/508

Augmenting Leakage Detection using Bootstrapping

Yuan Yao, Michael Tunstall, Elke De Mulder, Anton Kochepasov, and Patrick Schaumont

Abstract

Side-channel leakage detection methods based on statistical tests, such as t-test or chi^2-test, provide high confidence in the presence of leakage with a large number of traces. However, practical limitations on testing time and equipment may set an upper-bound on the number of traces available, turning the number of traces into a limiting factor in side-channel leakage detection. We describe a statistical technique, based on statistical bootstrapping, that significantly improves the effectiveness of leakage detection using a limited set of traces. Bootstrapping generates additional sample sets from an initial set by assuming that it is representative of the entire population. The additional sample sets are then used to conduct additional leakage detection tests, and we show how to combine the results of these tests. The proposed technique, applied to side-channel leakage detection, can significantly reduce the number of traces required to detect leakage by one, or more orders of magnitude. Furthermore, for an existing measured sample set, the method can significantly increase the confidence of existing leakage hypotheses over a traditional (non-bootstrap) leakage detection test. This paper introduces the bootstrapping technique for leakage detection, applies it to three practical cases, and describes techniques for its efficient computation.

Note: This paper is accepted by COSADE-2020 (https://www.cosade.org/)