Paper 2022/1201

Leakage Certification Made Simple

Aakash Chowdhury, University of Klagenfurt
Carlo Brunetta, Independent researcher
Arnab Roy, University of Innsbruck
Elisabeth Oswald, University of Birmingham, University of Klagenfurt
Abstract

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.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
A minor revision of an IACR publication in CRYPTO 2024
Keywords
Side channelsEvaluationLeakage CertificationMutual Information Estimation
Contact author(s)
aakash chowdhury @ aau at
brunocarletta @ gmail com
arnab roy @ uibk ac at
elisabeth oswald @ aau at
History
2024-06-28: last of 6 revisions
2022-09-12: received
See all versions
Short URL
https://ia.cr/2022/1201
License
Creative Commons Attribution-NonCommercial
CC BY-NC

BibTeX

@misc{cryptoeprint:2022/1201,
      author = {Aakash Chowdhury and Carlo Brunetta and Arnab Roy and Elisabeth Oswald},
      title = {Leakage Certification Made Simple},
      howpublished = {Cryptology {ePrint} Archive, Paper 2022/1201},
      year = {2022},
      url = {https://eprint.iacr.org/2022/1201}
}
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