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http://dx.doi.org/10.13089/JKIISC.2019.29.3.477

The Statistical Analysis of Differential Probability Using GPGPU Technology  

Jo, Eunji (Korea University)
Kim, Seong-Gyeom (Korea University)
Hong, Deukjo (Chonbuk University)
Sung, Jaechul (University of Seoul)
Hong, Seokhie (Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.29, no.3, 2019 , pp. 477-489 More about this Journal
Abstract
In this paper, we experimentally verify the expected differential probability under the markov cipher assumption and the distribution of the differential probability. Firstly, we validate the expected differential probability of 6round-PRESENT of the lightweight block cipher under the markov cipher assumption by analyzing the empirical differential probability. Secondly, we demonstrate that even though the expected differential probability under the markov cipher assumption seems valid, the empirical distribution does not follow the well-known distribution of the differential probability. The results was deduced from the 4round-GIFT. Finally, in order to analyze whether the key-schedule affects the mis-matching phenomenon, we collect the results while changing the XOR positions of round keys on GIFT. The results show that the key-schedule is not the only factor to affect the mis-matching phenomenon. Leveraging on GPGPU technology, the data collection process can be performed about 157 times faster than using CPU only.
Keywords
GPGPU; CUDA; Differential Cryptanalysis; GIFT; PRESENT; Markov Cipher; Statistical Analysis;
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