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Categorising and Comparing Cluster-Based DPA Distinguishers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication Selected Areas in Cryptography – SAC 2017
Subtitle of host publication24th International Conference, Ottawa, ON, Canada, August 16-18, 2017, Revised Selected Papers
Publisher or commissioning bodySpringer, Cham
Pages442-458
Number of pages17
ISBN (Electronic)9783319725659
ISBN (Print)9783319725642
DOIs
DateAccepted/In press - 7 Jul 2017
DatePublished (current) - 23 Dec 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743

Abstract

Side-channel distinguishers play an important role in differential power analysis, where real world leakage information is compared against hypothetical predictions in order to guess at the underlying secret key. A class of distinguishers which can be described as ‘cluster-based’ have the advantage that they are able to exploit multi-dimensional leakage samples in scenarios where only loose, ‘semi-profiled’ approximations of the true leakage forms are available. This is by contrast with univariate distinguishers exploiting only single points (e.g. correlation), and Template Attacks requiring concise fitted models which can be overly sensitive to mismatch between the profiling and attack acquisitions. This paper collects together—to our knowledge, for the first time—the various different proposals for cluster-based DPA (concretely, Differential Cluster Analysis, First Principal Components Analysis, and Linear Discriminant Analysis), and shows how they fit within the robust ‘semi-profiling’ attack procedure proposed by Whitnall et al. at CHES 2015. We provide discussion of the theoretical similarities and differences of the separately proposed distinguishers as well as an empirical comparison of their performance in a range of (real and simulated) leakage scenarios and with varying parameters. Our findings have application for practitioners contrained to rely on ‘semi-profiled’ models who wish to make informed choices about the best known procedures to exploit such information.

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