How to compare selections of points of interest for side-channel distinguishers in practice?

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Abstract

Side-channel distinguishers aim to reveal the secrets used in crypto devices by utilizing the subtle dependence between some sensitive intermediate values and physical leakages produced during its executions. For this purpose, one or more points of interest (POIs) corresponding to manipulations of one sensitive intermediate value are usually selected and then fed into distinguishers. However, it turns out in practice that POIs selected, even they are from the same leakage traces, will have significant impacts on the key recovery efficacy of distinguishers. Therefore, it makes a very practical sense to investigate the concrete impacts of POIs selections on side-channel distinguishers, and then pick out from those POIs selections available the most appropriate one for a certain distinguisher. In order to address these problems, we propose an evaluation framework for the analysis of POIs selections for side-channel distinguishers. Basically, our framework consists of two stages: the first stage captures the validity of points selected, while the second one reflects their quality with respect to a certain distinguisher. Specifically, on the one hand, in order to measure the goodness of one POIs selection, we introduce a quantitative metric of accuracy rate, from a perspective of statistics; on the other hand, we adopt the widely accepted security metric of success rate proposed by Standaert et al. at EUROCRYPT 2009 to reflect the quality of the points selected. Eventually, taking five typical POIs selections and three popular side-channel distinguishers as concrete study cases, we perform simulated attacks and practical attacks as well, the results of which not only fully justify our proposed methods but also reveal some interesting observations.

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APA

Zheng, Y., Zhou, Y., Yu, Z., Hu, C., & Zhang, H. (2015). How to compare selections of points of interest for side-channel distinguishers in practice? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8958, pp. 200–214). Springer Verlag. https://doi.org/10.1007/978-3-319-21966-0_15

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