Paper 2014/935
Boosting Higher-Order Correlation Attacks by Dimensionality Reduction
Nicolas Bruneau, Jean-Luc Danger, Sylvain Guilley, Annelie Heuser, and Yannick Teglia
Abstract
Multi-variate side-channel attacks allow to break higher-order masking protections by combining several leakage samples. But how to optimally extract all the information contained in all possible $d$-tuples of points? In this article, we introduce preprocessing tools that answer this question. We first show that maximizing the higher-order CPA coefficient is equivalent to finding the maximum of the covariance. We apply this equivalence to the problem of trace dimensionality reduction by linear combination of its samples. Then we establish the link between this problem and the Principal Component Analysis. In a second step we present the optimal solution for the problem of maximizing the covariance. We also theoretically and empirically compare these methods. We finally apply them on real measurements, publicly available under the DPA Contest v4, to evaluate how the proposed techniques improve the second-order CPA (2O-CPA).
Note: In this version, a more pedagogical explanation of the "modulated leakage" notion is given.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Published elsewhere. Minor revision. SPACE 2014
- DOI
- 10.1007/978-3-319-12060-7_13
- Contact author(s)
- sylvain guilley @ telecom-paristech fr
- History
- 2014-12-17: revised
- 2014-11-18: received
- See all versions
- Short URL
- https://ia.cr/2014/935
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2014/935,
author = {Nicolas Bruneau and Jean-Luc Danger and Sylvain Guilley and Annelie Heuser and Yannick Teglia},
title = {Boosting Higher-Order Correlation Attacks by Dimensionality Reduction},
howpublished = {Cryptology {ePrint} Archive, Paper 2014/935},
year = {2014},
doi = {10.1007/978-3-319-12060-7_13},
url = {https://eprint.iacr.org/2014/935}
}
