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Security Analysis of SKINNY under Related-Tweakey Settings (Long Paper)

Authors:
Guozhen Liu , Nanyang Technological University (Singapore); Shanghai Jiao Tong University
Mohona Ghosh , Nanyang Technological University (Singapore); Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Jabalpur
Ling Song , Nanyang Technological University (Singapore); State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences
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DOI: 10.13154/tosc.v2017.i3.37-72
URL: https://tosc.iacr.org/index.php/ToSC/article/view/765
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Abstract: In CRYPTO’16, a new family of tweakable lightweight block ciphers - SKINNY was introduced. Denoting the variants of SKINNY as SKINNY-n-t, where n represents the block size and t represents the tweakey length, the design specifies t ∈ {n, 2n, 3n}. In this work, we evaluate the security of SKINNY against differential cryptanalysis in the related-tweakey model. First, we investigate truncated related-tweakey differential trails of SKINNY and search for the longest impossible and rectangle distinguishers where there is only one active cell in the input and the output. Based on the distinguishers obtained, 19, 23 and 27 rounds of SKINNY-n-n, SKINNY-n-2n and SKINNY-n-3n can be attacked respectively. Next, actual differential trails for SKINNY under related-tweakey model are explored and optimal differential trails of SKINNY-64 within certain number of rounds are searched with an indirect searching method based on Mixed-Integer Linear Programming. The results show a trend that as the number of rounds increases, the probability of optimal differential trails is much lower than the probability derived from the lower bounds of active Sboxes in SKINNY.
BibTeX
@article{tosc-2017-28497,
  title={Security Analysis of SKINNY under Related-Tweakey Settings (Long Paper)},
  journal={IACR Trans. Symmetric Cryptol.},
  publisher={Ruhr-Universität Bochum},
  volume={2017, Issue 3},
  pages={37-72},
  url={https://tosc.iacr.org/index.php/ToSC/article/view/765},
  doi={10.13154/tosc.v2017.i3.37-72},
  author={Guozhen Liu and Mohona Ghosh and Ling Song},
  year=2017
}