Reference : Automated Truncation of Differential Trails and Trail Clustering in ARX
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/49785
Automated Truncation of Differential Trails and Trail Clustering in ARX
English
Biryukov, Alexei mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Cardoso Dos Santos, Luan mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Feher, Daniel mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Cryptolux >]
Velichkov, Vesselin mailto [University of Edinburgh]
Vitto, Giuseppe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Cryptolux >]
2021
No
[en] secret-key cryptography ; Symmetric-key ; Block Ciphers ; Differential Cryptanalysis ; Truncated Differentials ; ARX ; Speck
[en] We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We further describe a method to merge the set of truncated trails into a compact set of non-overlapping truncated trails with associated probability and we demonstrate the application of the tool on block cipher Speck64. We have also investigated the effect of clustering of differential trails around a fixed input trail. The best cluster that we have found for 15 rounds has probability 2^−55.03 (consisting of 389 unique output differences) which allows us to build a distinguisher using 128 times less data than the one based on just the single best trail, which has probability 2^−62. Moreover, we show examples for Speck64 where a cluster of trails around a suboptimal (in terms of probability) input trail results in higher overall probability compared to a cluster obtained around the best differential trail.
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/49785
https://eprint.iacr.org/2021/1194
FnR ; FNR11684537 > Alex Biryukov > FinCrypt > Security, Scalability, And Privacy In Blockchain Applications And Smart Contracts > 01/08/2018 > 31/07/2021 > 2017

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