International Association for Cryptologic Research

International Association
for Cryptologic Research


Differential-Linear Approximation Semi-Unconstrained Searching and Partition Tree: Application to LEA and Speck

Yi Chen , Institute for Advanced Study, Tsinghua University, Beijing, China
Zhenzhen Bao , Institute for Network Sciences and Cyberspace, Tsinghua University
Hongbo Yu , Department of Computer Science and Technology, Tsinghua University
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Presentation: Slides
Conference: ASIACRYPT 2023
Abstract: The differential-linear attack is one of the most effective attacks against ARX ciphers. However, two technical problems are preventing it from being more effective and having more applications: (1) there is no efficient method to search for good differential-linear approximations. Existing methods either have many constraints or are currently inefficient. (2) partitioning technique has great potential to reduce the time complexity of the key-recovery attack, but there is no general tool to construct partitions for ARX ciphers. In this work, we step forward in solving the two problems. First, we propose a novel idea for generating new good differential-linear approximations from known ones, based on which new searching algorithms are designed. Second, we propose a general tool named partition tree, for constructing partitions for ARX ciphers. Based on these new techniques, we present better attacks for two ISO/IEC standards, i.e., LEA and Speck. For LEA, we present the first 17-round distinguisher which is 1 round longer than the previous best distinguisher. Furthermore, we present the first key recovery attacks on 17-round LEA-128, 18-round LEA-192, and 18-round LEA-256, which attack 3, 4, and 3 rounds more than the previous best attacks. For Speck, we find better differential-linear distinguishers for Speck48 and Speck64. The first differential-linear distinguishers for Speck96 and Speck128 are also presented.
  title={Differential-Linear Approximation Semi-Unconstrained Searching and Partition Tree: Application to LEA and Speck},
  author={Yi Chen and Zhenzhen Bao and Hongbo Yu},