### Good is Not Good Enough: Deriving Optimal Distinguishers from Communication Theory

Annelie Heuser, Olivier Rioul, and Sylvain Guilley

##### Abstract

We find mathematically optimal side-channel distinguishers by looking at the side-channel as a communication channel. Our methodology can be adapted to any given scenario (device, signal-to-noise ratio, noise distribution, leakage model, etc.). When the model is known and the noise is Gaussian, the optimal distinguisher outperforms CPA and covariance. However, we show that CPA is optimal when the model is only known on a proportional scale. For non-Gaussian noise, we obtain different optimal distinguishers, one for each noise distribution. When the model is imperfectly known, we consider the scenario of a weighted sum of the sensitive variable bits where the weights are unknown and drawn from a normal law. In this case, our optimal distinguisher performs better than the classical linear regression analysis.

Note: Mentioning that "optimal statistical power analysis" (IACR ePrint: https://eprint.iacr.org/2003/152 ) was indeed ... correctly termed "optimal" (under some assumptions -- like noise Gaussianity and large number of traces with uniformly distributed plaintexts), in that Pearson Correlation is indeed the optimal statistical distinguisher.

##### Metadata
Available format(s)
Category
Applications
Publication info
A minor revision of an IACR publication in CHES 2014
Keywords
Side-channel analysisdistinguishercommunication channelmaxi- mum likelihoodcorrelation power analysisuniform noiseLaplacian noise.
Contact author(s)
sylvain guilley @ telecom-paristech fr
History
2015-01-06: last of 4 revisions
2014-07-07: received
See all versions
Short URL
https://ia.cr/2014/527
License

CC BY

BibTeX

@misc{cryptoeprint:2014/527,
author = {Annelie Heuser and Olivier Rioul and Sylvain Guilley},
title = {Good is Not Good Enough: Deriving Optimal Distinguishers from Communication Theory},
howpublished = {Cryptology ePrint Archive, Paper 2014/527},
year = {2014},
note = {\url{https://eprint.iacr.org/2014/527}},
url = {https://eprint.iacr.org/2014/527}
}

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