Paper 2022/493
Don’t Learn What You Already Know: Scheme-Aware Modeling for Profiling Side-Channel Analysis against Masking
Abstract
Over the past few years, deep-learning-based attacks have emerged as a de facto standard, thanks to their ability to break implementations of cryptographic primitives without pre-processing, even against widely used counter-measures such as hiding and masking. However, the recent works of Bronchain and Standaert at Tches 2020 questioned the soundness of such tools if used in an uninformed setting to evaluate implementations protected with higher-order masking. On the opposite, worst-case evaluations may be seen as possibly far from what a real-world adversary could do, thereby leading to too conservative security bounds. In this paper, we propose a new threat model that we name scheme-aware benefiting from a trade-off between uninformed and worst-case models. Our scheme-aware model is closer to a real-world adversary, in the sense that it does not need to have access to the random nonces used by masking during the profiling phase like in a worst-case model, while it does not need to learn the masking scheme as implicitly done by an uninformed adversary. We show how to combine the power of deep learning with the prior knowledge of scheme-aware modeling. As a result, we show on simulations and experiments on public datasets how it sometimes allows to reduce by an order of magnitude the profiling complexity, i.e., the number of profiling traces needed to satisfyingly train a model, compared to a fully uninformed adversary.
Note: Adding Acknowledgements
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published by the IACR in TCHES 2023
- Keywords
- Deep Learning Side Channel Analysis Masking Black Box Grey Box Profiling
- Contact author(s)
-
loic masure @ uclouvain be
maxime lecomte @ cea fr
fstandae @ uclouvain be - History
- 2022-10-11: revised
- 2022-04-23: received
- See all versions
- Short URL
- https://ia.cr/2022/493
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2022/493,
author = {Loïc Masure and Valence Cristiani and Maxime Lecomte and François-Xavier Standaert},
title = {Don’t Learn What You Already Know: Scheme-Aware Modeling for Profiling Side-Channel Analysis against Masking},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/493},
year = {2022},
url = {https://eprint.iacr.org/2022/493}
}