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Optimal Side-Channel Attacks for Multivariate Leakages and Multiple Models

Nicolas Bruneau 1, 2 Sylvain Guilley 1, 2 Annelie Heuser 3, 2 Damien Marion 1, 2 Olivier Rioul 3, 2
1 SSH - Secure and Safe Hardware
LTCI - Laboratoire Traitement et Communication de l'Information
3 COMNUM - Communications Numériques
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : Side-channel attacks allow to extract secret keys from embedded systems like smartcards or smartphones. In practice, the side-channel signal is measured as a trace consisting of several samples. Also, several sensitive bits are manipulated in parallel, each leaking differently. Therefore, the informed attacker needs to devise side-channel distinguishers that can handle both multivariate leakages and multiple models. In the state-of-the-art, these two issues have two independent solutions: on the one hand, dimensionality reduction can cope with multivariate leakage; on the other hand, on- line stochastic approach can cope with multiple models. In this paper, we combine both solutions to derive closed-form expressions of the resulting optimal distin- guisher in terms of matrix operations, in all situations where the model can be either profiled offline or regressed online. Optimality here means that the success rate is maximized for a given number of traces. We recover known results for uni- and bi-variate mod- els (including correlation power analysis), and investigate novel distinguishers for multiple models with more than two parameters. In addition, following ideas from the AsiaCrypt’2013 paper “Behind the Scene of Side-Channel Attacks”, we provide fast computation algo- rithms in which the traces are accumulated prior to computing the distinguisher values.
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Submitted on : Friday, September 13, 2019 - 5:08:34 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:54 PM


  • HAL Id : hal-02287606, version 1


Nicolas Bruneau, Sylvain Guilley, Annelie Heuser, Damien Marion, Olivier Rioul. Optimal Side-Channel Attacks for Multivariate Leakages and Multiple Models. Journal of Cryptographic Engineering, 2017, 7 (4), pp.331-341. ⟨hal-02287606⟩



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