Paper 2023/109 Original: eprint.iacr.org/2023/109 ↗
SoK: Modeling for Large S-boxes Oriented to Differential Probabilities and Linear Correlations (Long Paper)
Abstract
Automatic methods for differential and linear characteristic search are well-established at the moment. Typically, the designers of novel ciphers also give preliminary analytical findings for analysing the differential and linear properties using automatic techniques. However, neither MILP-based nor SAT/SMT-based approaches have fully resolved the problem of searching for actual differential and linear characteristics of ciphers with large S-boxes. To tackle the issue, we present three strategies for developing SAT models for 8-bit S-boxes that are geared toward differential probabilities and linear correlations. While these approaches cannot guarantee a minimum model size, the time needed to obtain models is drastically reduced. The newly proposed SAT model for large S-boxes enables us to establish that the upper bound on the differential probability for 14 rounds of SKINNY-128 is 2^{-131}, thereby completing the unsuccessful work of Abdelkhalek et al. We also analyse the seven AES-based constructions C1 - C7 designed by Jean and Nikolic and compute the minimum number of active S-boxes necessary to cause an internal collision using the SAT method. For two constructions C3 and C5, the current lower bound on the number of active S-boxes is increased, resulting in a more precise security analysis for these two structures.
Metadata
- Available format(s)
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- History
- 2023-01-28: last updated
- 2023-01-28: published
- Short URL
- https://ia.cr/2023/109
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2023/109,
author = {Ling Sun and Meiqin Wang},
title = {SoK: Modeling for Large S-boxes Oriented to Differential Probabilities and Linear Correlations (Long Paper)},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/109},
year = {2023},
url = {https://eprint.iacr.org/2023/109}
}