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Journal Articles Artificial Intelligence Year : 2020

Computing AES related-key differential characteristics with constraint programming

Abstract

Cryptanalysis aims at testing the properties of encryption processes, and this usually implies solving hard optimization problems. In this paper, we focus on related-key differential attacks for the Advanced Encryption Standard (AES), which is the encryption standard for block ciphers. To mount these attacks, cryptanalysts need to solve the optimal related-key differential characteristic problem. Dedicated approaches do not scale well for this problem, and need weeks to solve its hardest instances. In this paper, we improve existing Constraint Programming (CP) approaches for computing optimal related-key differential characteristics: we add new constraints that detect inconsistencies sooner, and we introduce a new decomposition of the problem in two steps. These improvements allow us to compute all optimal related-key differential characteristics for AES-128, AES-192 and AES-256 in a few hours.
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Dates and versions

hal-02327893 , version 1 (23-10-2019)

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David Gérault, Pascal Lafourcade, Marine Minier, Christine Solnon. Computing AES related-key differential characteristics with constraint programming. Artificial Intelligence, 2020, 278, pp.103183 (24). ⟨10.1016/j.artint.2019.103183⟩. ⟨hal-02327893⟩
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