International Association for Cryptologic Research

International Association
for Cryptologic Research

Transactions on Cryptographic Hardware and Embedded Systems, Volume 2023

MCRank: Monte Carlo Key Rank Estimation for Side-Channel Security Evaluations


Giovanni Camurati
ETH Zurich, Zurich, Switzerland

Matteo Dell’Amico
University of Genoa, Genoa, Italy

François-Xavier Standaert
UC Louvain, Louvain, Belgium


Keywords: Key rank estimation, Side channel attacks, Monte Carlo methods


Abstract

Key rank estimation provides a measure of the effort that the attacker has to spend bruteforcing the key of a cryptographic algorithm, after having gained some information from a side channel attack. We present MCRank, a novel method for key rank estimation based on Monte Carlo sampling. MCRank provides an unbiased estimate of the rank and a confidence interval. Its bounds rapidly become tight for increasing sample size, with a corresponding linear increase of the execution time. When applied to evaluate an AES-128 implementation, MCRank can be orders of magnitude faster than the state-of-the-art histogram-based enumeration method for comparable bound tightness. It also scales better than previous work for large keys, up to 2048 bytes. Besides its conceptual simplicity and efficiency, MCRank can assess for the first time the security of large keys even if the probability distributions given the side channel leakage are not independent between subkeys, which occurs, for example, when evaluating the leakage security of an AES-256 implementation.

Publication

Transactions of Cryptographic Hardware and Embedded Systems, Volume 2023, Issue 1

Paper

Artifact

Artifact number
tches/2023/a2

Artifact published
September 2, 2023

README

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License
GPLv3 This work is licensed under the GNU General Public License version 3.

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BibTeX How to cite

Camurati, G., Dell’Amico, M., & Standaert, F.-X. (2022). MCRank: Monte Carlo Key Rank Estimation for Side-Channel Security Evaluations. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2023(1), 277–300. https://doi.org/10.46586/tches.v2023.i1.277-300. Artifact at https://artifacts.iacr.org/tches/2023/a2.