Cryptology ePrint Archive: Report 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.
Category / Keywords: applications / Side-channel analysis, distinguisher, communication channel, maxi- mum likelihood, correlation power analysis, uniform noise, Laplacian noise.
Original Publication (with minor differences): IACR-CHES-2014
Date: received 6 Jul 2014, last revised 19 Sep 2014
Contact author: annelie heuser at telecom-paristech fr
Available format(s): PDF | BibTeX Citation
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.
Version: 20140919:083320 (All versions of this report)
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