A New Method To Find All The High-Probability Word-Oriented Truncated Differentials: Application To Midori, SKINNY And CRAFT

Hao Guo, Zhiyu Zhang, Qianqian Yang, Lei Hu, Yiyuan Luo
{"title":"A New Method To Find All The High-Probability Word-Oriented Truncated Differentials: Application To Midori, SKINNY And CRAFT","authors":"Hao Guo, Zhiyu Zhang, Qianqian Yang, Lei Hu, Yiyuan Luo","doi":"10.1093/comjnl/bxab213","DOIUrl":null,"url":null,"abstract":"This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is D , then D r contains all the differential probabilities of an r -round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix T . Regardless of the effect of the S -box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate T r using a workstation. Then all the r -round truncated differential probabilities can be found from T r . And the probability in T r is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as T r stores all the truncated differential probabilities, we can also find all the impossible truncated differentials","PeriodicalId":21872,"journal":{"name":"South Afr. Comput. J.","volume":"279 1","pages":"1069-1082"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"South Afr. Comput. J.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/comjnl/bxab213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is D , then D r contains all the differential probabilities of an r -round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix T . Regardless of the effect of the S -box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate T r using a workstation. Then all the r -round truncated differential probabilities can be found from T r . And the probability in T r is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as T r stores all the truncated differential probabilities, we can also find all the impossible truncated differentials
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种寻找所有高概率面向词的截断微分的新方法:在Midori、SKINNY和CRAFT中的应用
本文提出了一种利用矩阵乘法求高概率截断微分的新方法。对于具有相似round函数的Markov密码,设round函数的转移概率矩阵为D,则D r包含一个r -round分组密码的所有微分概率。为了降低矩阵维数,我们考虑了面向词的截断微分矩阵和截断转移概率矩阵T。不考虑S盒的影响,我们关注的是一个单元格上是否存在非零的差异,而不是差异的值。在这种情况下,矩阵维数显著减少,我们可以使用工作站计算tr。然后从T r可以求出所有r圆截断的微分概率。T r中的概率是整个截尾微分船体的概率,而不是单个或多个截尾微分特征的概率。此外,我们还对轻量级分组密码的截断微分进行了更精确的概率估计。结合截尾型差动船体,发现了一些较长的截尾型差动隔振器。由于T r存储了所有截断的微分概率,我们也可以找到所有不可能的截断的微分
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Taylor Sun Flower Optimization-Based Compressive Sensing for Image Compression and Recovery Special Issue on Failed Approaches and Insightful Losses in Cryptology - Foreword Role of Machine Learning on Key Extraction for Data Privacy Preservation of Health Care Sectors in IoT Environment Incorrectly Generated RSA Keys: How I Learned To Stop Worrying And Recover Lost Plaintexts Smart Multimedia Compressor - Intelligent Algorithms for Text and Image Compression
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1