• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Qu Longjiang, Chen Xi, Niu Tailin, Li Chao. Recent Progress in Low Differential Uniformity Functions over Finite Fields[J]. Journal of Computer Research and Development, 2018, 55(9): 1931-1945. DOI: 10.7544/issn1000-1239.2018.20180159
Citation: Qu Longjiang, Chen Xi, Niu Tailin, Li Chao. Recent Progress in Low Differential Uniformity Functions over Finite Fields[J]. Journal of Computer Research and Development, 2018, 55(9): 1931-1945. DOI: 10.7544/issn1000-1239.2018.20180159

Recent Progress in Low Differential Uniformity Functions over Finite Fields

More Information
  • Published Date: August 31, 2018
  • To prevent differential attack on the cipher, cryptographic functions are required to have low differential uniformity. Perfect nonlinear (PN) functions, almost perfect nonlinear (APN) functions and differentially 4-uniform permutations are the most important cryptographic functions with low differential uniformity. Here we survey the recent main research results about cryptographic functions with low differential uniformity such as PN functions, APN functions and differentially 4-uniform permutations. First, we recall the connections between PN functions and the mathematical objects such as the semifield, which survey the known constructions of PN functions and the pseudo-planar functions. Second, the properties and judgement of APN functions are analyzed. We also list the known constructions of APN functions and recall the inequivalent results between them. Third, we summarize the known results on the constructions of differentially 4-uniform permutations and discuss their equivalence. Then, we recall the applications of low differential uniformity functions in the design of actual ciphers. Lastly, we propose some research problems on cryptographic functions with low differential uniformity.
  • Related Articles

    [1]Shang Jing, Wu Zhihui, Xiao Zhiwen, Zhang Yifei. Graph4Cache: A Graph Neural Network Model for Cache Prefetching[J]. Journal of Computer Research and Development, 2024, 61(8): 1945-1956. DOI: 10.7544/issn1000-1239.202440190
    [2]Zhang Tianming, Zhao Jie, Jin Lu, Chen Lu, Cao Bin, Fan Jing. Vertex Betweenness Centrality Computation Method over Temporal Graphs[J]. Journal of Computer Research and Development, 2023, 60(10): 2383-2393. DOI: 10.7544/issn1000-1239.202220650
    [3]Li Fengying, Shen Huiqiang, Dong Rongsheng. Compact Representation of Temporal Graphs Based on kd-MDD[J]. Journal of Computer Research and Development, 2022, 59(6): 1286-1296. DOI: 10.7544/issn1000-1239.20200856
    [4]Zhang Tianming, Xu Yiheng, Cai Xinwei, Fan Jing. A Shortest Path Query Method over Temporal Graphs[J]. Journal of Computer Research and Development, 2022, 59(2): 362-375. DOI: 10.7544/issn1000-1239.20210893
    [5]Zhang Heng, Zhang Libo, WuYanjun. Large-Scale Graph Processing on Multi-GPU Platforms[J]. Journal of Computer Research and Development, 2018, 55(2): 273-288. DOI: 10.7544/issn1000-1239.2018.20170697
    [6]Dong Rongsheng, Zhang Xinkai, Liu Huadong, Gu Tianlong. Representation and Operations Research of k\+2-MDD in Large-Scale Graph Data[J]. Journal of Computer Research and Development, 2016, 53(12): 2783-2792. DOI: 10.7544/issn1000-1239.2016.20160589
    [7]Yu Jing, Liu Yanbing, Zhang Yu, Liu Mengya, Tan Jianlong, Guo Li. Survey on Large-Scale Graph Pattern Matching[J]. Journal of Computer Research and Development, 2015, 52(2): 391-409. DOI: 10.7544/issn1000-1239.2015.20140188
    [8]Fu Lizhen, Meng Xiaofeng. Reachability Indexing for Large-Scale Graphs: Studies and Forecasts[J]. Journal of Computer Research and Development, 2015, 52(1): 116-129. DOI: 10.7544/issn1000-1239.2015.20131567
    [9]Zhong Ming, Wang Sheng, and Liu Mengchi. An Optimization Approach of Known-Item Search on Large-Scale Graph Data[J]. Journal of Computer Research and Development, 2014, 51(1): 54-63.
    [10]Xu Shifeng, Gao Jun, Yang Dongqing, and Wang Tengjiao. Pass-Count-Based Path Query on Big Graph Datasets[J]. Journal of Computer Research and Development, 2010, 47(1): 96-103.
  • Cited by

    Periodical cited type(10)

    1. 刘晨曦,孙秉珍,楚晓丽,祁畅. 基于复合粗糙集的异构属性患者社区划分模型. 复杂系统与复杂性科学. 2023(03): 27-34 .
    2. 孙学良,王巍,黄俊恒,辛国栋,王佰玲. 基于标签传播的两阶段社区检测算法. 网络与信息安全学报. 2022(02): 139-149 .
    3. 郑文萍,乔艳超,杨贵. 基于局部邻域连通性的重叠社区发现算法. 山西大学学报(自然科学版). 2022(02): 369-379 .
    4. 张霄宏,史爱静,贾慧娟,任建吉. 一种优化的标签传播方法. 小型微型计算机系统. 2021(01): 137-141 .
    5. 郑文萍,刘美麟,穆俊芳,杨贵. 一种基于节点稳定性的社区发现算法. 南京大学学报(自然科学). 2021(01): 101-109 .
    6. 吴卫江,桑睿彤,郑艺峰. 基于限制性随机游走局部谱近似社区发现算法. 计算机工程与设计. 2021(09): 2472-2477 .
    7. 赵霞,张泽华,张晨威,李娴. RGNE:粗糙粒化的网络嵌入式重叠社区发现方法. 计算机研究与发展. 2020(06): 1302-1311 . 本站查看
    8. 闵磊. 基于社区发现的个性化推荐技术研究. 科技资讯. 2020(30): 217-218+225 .
    9. 郑文萍,岳香豆,杨贵. 基于随机游走的改进标签传播算法. 计算机应用. 2020(12): 3423-3429 .
    10. 凤丽洲,覃悦,杨贵军. 节点局部Fiedler向量中心性差值社区发现算法. 计算机科学与探索. 2019(12): 2029-2042 .

    Other cited types(27)

Catalog

    Article views (1229) PDF downloads (381) Cited by(37)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return