Paper 2017/764

Categorising and Comparing Cluster-Based DPA Distinguishers

Xinping Zhou, Carolyn Whitnall, Elisabeth Oswald, Degang Sun, and Zhu Wang


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 constrained to rely on `semi-profiled' models who wish to make informed choices about the best known procedures to exploit such information.

Available format(s)
Publication info
Published elsewhere. Selected Areas in Cryptography, 2017
side-channel analysisDPAmachine learning
Contact author(s)
carolyn whitnall @ bristol ac uk
2017-08-08: received
Short URL
Creative Commons Attribution


      author = {Xinping Zhou and Carolyn Whitnall and Elisabeth Oswald and Degang Sun and Zhu Wang},
      title = {Categorising and Comparing Cluster-Based DPA Distinguishers},
      howpublished = {Cryptology ePrint Archive, Paper 2017/764},
      year = {2017},
      note = {\url{}},
      url = {}
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