Paper 2017/188
Division Cryptanalysis of Block Ciphers with a Binary Diffusion Layer
Wenying Zhang and Vincent Rijmen
Abstract
In this paper, we propose an accurate security evaluation methodology for block ciphers with a binary diffusion layers against division cryptanalysis. We illustrate the division property by the independence of variables, and exploit a one-to-one mapping between division trails and invertible sub-matrices. We give a new way to model the propagation of division property of linear diffusion layers by the smallest amount of inequalities which are generated from linear combinations of row vectors of the diffusion matrix. The solutions of these inequalities are exactly the division trails of linear transformation. Hence the description is compact and optimal. As applications of our methodology, we first present a 10-round integral distinguisher for Skinny, proposed at CRYPTO 2016 which is of one round more than that found by using the previous method. For Midori, proposed at ASIACRYPT 2015, the designers have obtained a 3.5-round integral characteristic. Surprisingly, we find 7-round integral distinguishers both for Midori64 and Midori128. Most importantly, we obtain the longest integral distinguishers for block ciphers with a binary diffusion layer. It seems that any more improvement of this kind of integral distinguishers using the division property is impossible. Therefore, the technique can be used to prove security against division cryptanalysis, and we can hopefully expect it to become a useful technique for designers.
Metadata
- Available format(s)
- Publication info
- Preprint. MINOR revision.
- Contact author(s)
- wzhang @ esat kuleuven be
- History
- 2017-02-28: revised
- 2017-02-28: received
- See all versions
- Short URL
- https://ia.cr/2017/188
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2017/188, author = {Wenying Zhang and Vincent Rijmen}, title = {Division Cryptanalysis of Block Ciphers with a Binary Diffusion Layer}, howpublished = {Cryptology {ePrint} Archive, Paper 2017/188}, year = {2017}, url = {https://eprint.iacr.org/2017/188} }