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Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition.

Authors :
Fang, Xing
Zhang, Hongxin
Wang, Danzhi
Yan, Hao
Fan, Fan
Shu, Lei
Source :
Entropy. Nov2022, Vol. 24 Issue 11, p1508. 18p.
Publication Year :
2022

Abstract

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption, and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10994300
Volume :
24
Issue :
11
Database :
Academic Search Index
Journal :
Entropy
Publication Type :
Academic Journal
Accession number :
160144950
Full Text :
https://doi.org/10.3390/e24111508