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基于GPU的大状态密码S盒差分性质评估方法

张润莲,张密,武小年,舒瑞   

  1. 广西密码学与信息安全重点实验室（桂林电子科技大学）
  • 收稿日期:2023-09-15 修回日期:2023-12-29 发布日期:2024-01-31 出版日期:2024-01-31
  • 通讯作者: 张润莲
  • 作者简介:张润莲（1974—）,女，山西介休人，副教授，博士，主要研究方向：信息安全、分布式计算；张密（1998—），女，贵州毕节人，硕士研究生，主要研究方向：信息安全；武小年（1972—），男，湖北监利人，教授，硕士，主要研究方向：信息安全、分布式计算；舒瑞（1998—），男，四川广安人，硕士研究生，主要研究方向：信息安全。
  • 基金资助:
    国家自然科学基金资助项目(62062026); 广西重点研发计划(桂科AB23026131); 广西创新研究团队项目(2019GXNSFGA245004)

Differential property evaluation method based on GPU for large-state cryptographic S-boxes

ZHANG Runlian, ZHANG Mi, WU Xiaonian, SHU Rui   

  1. (Guangxi Key Laboratory of Cryptography and Information Security) Guilin University of Electronic Technology
  • Received:2023-09-15 Revised:2023-12-29 Online:2024-01-31 Published:2024-01-31
  • Contact: Runlian ZHANG
  • About author:ZHANG Runlian, born in 1974, Ph. D., associate professor. Her research interests include information security, distribution computing. ZHANG Mi, born in 1998, M. S. candidate. Her research interests include information security. WU Xiaonian, born in 1972, M. S., professor. His research interests include information security, distribution computing. SHU Rui, born in 1998, M. S. candidate. His research interests include information security.
  • Supported by:
    National Natural Science Foundation of China (62062026), Key Research and Development Program of Guangxi in China (guike AB23026131), Innovation Research Team Project of Guangxi in China (2019GXNSFGA245004).

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摘要： 大状态的密码S盒能够为对称密码算法提供更好的混淆性，但对大状态S盒的性质评估开销巨大。为高效评估大状态密码S盒的差分性质，提出基于GPU并行计算的大状态密码S盒差分性质评估方法。该方法基于现有的差分均匀度计算方法，针对16比特S盒的差分均匀度和32比特S盒的差分性质，分别设计GPU并行方案，通过优化GPU并行粒度和负载均衡提高了核函数和GPU的执行效率，并降低了计算时间。测试结果表明，相较于现有的CPU方法和GPU并行方法，所提方法实现大状态S盒差分性质评估的计算时间大大降低，提高了对大状态S盒差分性质的评估效率，对16比特S盒差分均匀度的计算时间约0.3 min；对32比特S盒的单个输入差分的最大输出差分概率计算时间约5 min，对它的差分性质计算时间约2.6 h。

关键词: 密码S盒, 差分密码分析, 差分均匀度, 最大输出差分概率, GPU并行计算

Abstract: Large-state cryptographic S-box can provide better obfuscation for symmetric encryption algorithm, but the cost for evaluating its properties is very expensive. To efficiently evaluate the differential properties of large-state cryptographic S-boxes, a GPU-based method for evaluating the differential properties of large-state cryptographic S-boxes was proposed. According to the existing differential uniformity calculation method, the GPU parallel schemes are designed for evaluating differential uniformity of 16-bit S-boxes and differential properties of 32-bit S-boxes respectively. The execution efficiency of kernel functions and GPU were improved by the schemes, and the time cost was reduced by optimizing GPU parallel granularity and load balancing. The tested results show that, compared with existing CPU methods and GPU parallel methods, the time cost of the schemes evaluated the differential properties of large-state cryptographic S-boxes is greatly reduced. The computation time for the differential uniformity of 16-bit S-boxes is about 0.3 min, the computation time for the maximum output differential probability of a single input differential of 32-bit S-boxes is about 5 min, and it is about 2.6 h for evaluating its differential properties.

Key words: cryptographic S-box, differential cryptanalysis, differential uniformity, maximum output differential probability, GPU parallel computing

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