Clustering of differentials in CRAFT with correlation matrices. Issue 12 (16th September 2022)

• Record Type: Journal Article
• Authors: Liu, Huimin; Zhang, Wenying; Zhang, Jinjiao; Sun, Xiaomeng
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 12113
• Page End: 12134
• Publication Date: 2022-09-16
• Subjects: correlation matrix -- CRAFT -- differential cryptanalysis -- Matsui's bounding condition -- SAT method; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.23078
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: CRAFT is an substitution‐permutation network tweakable block cipher proposed at fast software encryption 2019 by Beierle et al., which is designed to optimize the efficient protection against differential fault analysis (DFA) attacks. In this paper, the full round differential characteristics for CRAFT block cipher are given. A new method on counting the number of differentials by using correlation matrix is given. We can compute the number of all optimal characteristics or suboptimum differentials with the same input difference and output difference by hand. We explore the multiple differential trails and compute the probability of differential characteristic by using the multiplication of correlation matrices. Our work complements automatic search methods for the best differential with a careful manual analysis. Since the automatic search method is stranded by storage and search space limitations, which will cause a computer to crash as the number of search rounds increases. Thanks to the correlation matrix technique, we are able to find differential distinguishers for 9‐round of the cipher with the probability of at least 2 − 40.68 + 2 − 48.60 ${2}^{-40.68}+{2}^{-48.60}$ . Moreover, we can construct differential distinguisher covers more rounds based on the 9‐round differential distinguishers. As one of its typical application, we propose the differential characteristics for the full‐round CRAFT which ensure that the probability of each round is optimal. Besides, we explore the clustering effect on the full round by exhibiting a class of high probability characteristics for 9‐round. In general, we obtain a good understanding of the propagation of differences for CRAFT due to its algebraic structure.