Automatic Search Model for Related-Tweakey Impossible Differential Cryptanalysis

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Abstract

The design and analysis of dedicated tweakable block ciphers constitute a dynamic and relatively recent research field in symmetric cryptanalysis. The assessment of security in the related-tweakey model is of utmost importance owing to the existence of a public tweak. This paper proposes an automatic search model for identifying related-tweakey impossible differentials based on the propagation of states under specific constraints, which is inspired by the research of Hu et al. in ASIACRYPT 2020. Our model is universally applicable to block ciphers, but its search efficiency may be limited in some cases. To address this issue, we introduce the Locality Constraint Analysis (LCA) technique to impossible differential cryptanalysis and propose a generalized automatic search model. Technically, we transform our models into Satisfiability Modulo Theories (SMT) problems and solve them using the STP solver. We have applied our tools to several tweakable block ciphers, such as Joltik-BC, SKINNY, QARMA, and CRAFT, to evaluate their effectiveness and practicality. Specifically, we have discovered 7-round related-tweakey impossible differentials for Joltik-BC-192, and 12-round related-tweak impossible differentials, as well as 15-round related-tweakey impossible differentials for CRAFT for the first time. Based on the search results, we demonstrate that the LCA technique can be effectively performed when searching and determining the contradictory positions for the distinguisher with long trails or ciphers with large sizes in impossible differential cryptanalysis.

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APA

Chen, H., Li, Y., Hu, X., Liu, Z., Jiao, L., & Wang, M. (2023). Automatic Search Model for Related-Tweakey Impossible Differential Cryptanalysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13907 LNCS, pp. 3–22). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-41181-6_1

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