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Paper 2014/527

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

Annelie Heuser and 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: Added precisions about the "Bayesian" approach: the key to be decoded, in the digital communication vocabulary (or guessed, in the side-channel attack vocabulary), is consistently presented as a uniformly distributed random variable.

Metadata
Available format(s)
PDF
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)
annelie heuser @ telecom-paristech fr
History
2015-01-06: last of 4 revisions
2014-07-07: received
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Short URL
https://ia.cr/2014/527
License
Creative Commons Attribution
CC BY
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