Paper 2025/1670

Mixderive: A New Framework of Deriving Linear Approximations and Improved Differential-Linear Distinguishers for ChaCha

Zhengting Li, Information Engineering University
Lin Ding, Information Engineering University
Xinhai Wang, Information Engineering University
Jiang Wan, Information Engineering University
Abstract

ChaCha is a well-known ARX-based cipher and has become one of the most widely used ciphers in the real world. In this paper, a systematic three-case framework called \emph{Mixderive} to find linear approximations for ChaCha is proposed. By this new framework, new linear approximations for 3.5- and 4-round ChaCha are found, which are significantly better than the existing linear approximations proposed at EUROCRYPT 2021 and ASIACRYPT 2022. These improvements confirm the effectiveness of \emph{Mixderive}. In addition, new 2- and 2.5-round linear approximations for ChaCha are found by \emph{Mixderive}. Based on these new findings, new differential-linear distinguishers for 7- and 7.5-round ChaCha256 with complexities ${2^{162.28}}$ and ${2^{247.08}}$ are proposed, which improve the best known distinguishers by factors of ${2^{4.61}}$ and ${2^{4.46}}$, respectively. To the best of our knowledge, both cryptanalytic results are the best.

Metadata
Available format(s)
PDF
Category
Attacks and cryptanalysis
Publication info
Published elsewhere. Major revision. ISPEC 2025
Keywords
CryptanalysisLinear approximationDifferential-linear distinguisherChaChaStream cipher
Contact author(s)
dinglin_cipher @ 163 com
History
2025-09-18: approved
2025-09-15: received
See all versions
Short URL
https://ia.cr/2025/1670
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2025/1670,
      author = {Zhengting Li and Lin Ding and Xinhai Wang and Jiang Wan},
      title = {Mixderive: A New Framework of Deriving Linear Approximations and Improved Differential-Linear Distinguishers for {ChaCha}},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/1670},
      year = {2025},
      url = {https://eprint.iacr.org/2025/1670}
}
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