Leakage Assessment through Neural Estimation of the Mutual Information

Valence Cristiani; Maxime Lecomte; Philippe Maurine

doi:10.1007/978-3-030-61638-0_9

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Leakage Assessment through Neural Estimation of the Mutual Information

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Leakage Assessment through Neural Estimation of the Mutual Information

Valence Cristiani, Maxime Lecomte et Philippe Maurine

Applied Cryptography and Network Security Workshops ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy, October 19–22, 2020, Proceeding, Vol.12418, pp.144-162

Lecture Notes in Computer Science

ACNS 2020 - International Conference on Applied Cryptography and Network Security (Rome, Italy, 19/10/2020–22/10/2020)

14/10/2020

DOI: https://doi.org/10.1007/978-3-030-61638-0_9

Résumé

Side channel analysis

Mutual information

Deep learning

Hardware security

A large variety of side-channel attacks have been developed to extract secrets from electronic devices through their physical leakages. Whatever the utilized strategy, the amount of information one could gain from a side-channel trace is always bounded by the Mutual Information (MI) between the secret and the trace. This makes it, all punning aside, a key quantity for leakage evaluation. Unfortunately, traces are usually of too high dimension for existing statistical estimators to stay sound when computing the MI over full traces. However, recent works from the machine learning community have shown that it is possible to evaluate the MI in high dimensional space thanks to newest deep learning techniques. This paper explores how this new estimator could impact the side channel domain. It presents an analysis which aim is to derive the best way of using this estimator in practice. Then, it shows how such a tool can be used to assess the leakage of any device.

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Titre: Leakage Assessment through Neural Estimation of the Mutual Information
Créateurs - sans rôle: Valence Cristiani - CEA Grenoble
Maxime Lecomte - Institut polytechnique de Grenoble
Philippe Maurine - Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Détails de publication: Applied Cryptography and Network Security Workshops ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy, October 19–22, 2020, Proceeding, Vol.12418, pp.144-162
Colloque: ACNS 2020 - International Conference on Applied Cryptography and Network Security (Rome, Italy, 19/10/2020–22/10/2020)
Publications en série: Lecture Notes in Computer Science
Identifiants: 99151994609311
Unité académique: Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - LIRMM
Langue: English
Type de ressource: Conference proceeding
Champs locaux: hal-02980501