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A CPU-GPU-based parallel search algorithm for the best differential characteristics of block ciphers

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Abstract

The differential characteristics with high probability are critical for differential cryptanalysis. The process of searching such differential characteristics, especially the best one, is time-consuming. We believe that the modern hybrid computing systems can be used to accelerate the search process. However, to the best of our knowledge, the existing solutions are not designed for heterogeneous architectures. In this paper, we propose a parallel search algorithm for the best differential characteristic. Our method can be applied to any substitution–permutation network (SPN) block ciphers after making minor modifications. We implemented the proposed parallel search algorithm for PRESENT block cipher and also a sequential version, which based on the classic Matsui’s method, for comparison. The experimental result shows that the parallel algorithm using both CPU and GPU can achieve at least 4.4x and up to 18x speed-up compared to the sequential version.

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant No. 61702212).

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  1. Central China Normal University, Wuhan, China

    Pei Li, Shihao Zhou & Jiageng Chen

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  1. Pei Li
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  2. Shihao Zhou
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  3. Jiageng Chen
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Correspondence to Jiageng Chen.

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Li, P., Zhou, S. & Chen, J. A CPU-GPU-based parallel search algorithm for the best differential characteristics of block ciphers. J Supercomput 77, 11460–11480 (2021). https://doi.org/10.1007/s11227-021-03703-w

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