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Probabilistic Linearization: Internal Differential Collisions in up to 6 Rounds of SHA-3

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Advances in Cryptology – CRYPTO 2024 (CRYPTO 2024)

Abstract

The \(\texttt {SHA}\text {-} \texttt {3}\) standard consists of four cryptographic hash functions, called SHA3-224, SHA3-256, SHA3-384 and SHA3-512, and two extendable-output functions (XOFs), called SHAKE128 and \(\texttt {SHAKE256}\). In this paper, we study the collision resistance of the \(\texttt {SHA}\text {-} \texttt {3}\) instances. By analyzing the nonlinear layer, we introduce the concept of maximum difference density subspace, and develop a new target internal difference algorithm by probabilistic linearization. We also exploit new strategies for optimizing the internal differential characteristic. Furthermore, we figure out the expected size of collision subsets in internal differentials, by analyzing the collision probability of the digests rather than the intermediate states input to the last nonlinear layer. These techniques enhance the analysis of internal differentials, leading to the best collision attacks on four round-reduced variants of the \(\texttt {SHA}\text {-} \texttt {3}\) instances. In particular, the number of attacked rounds is extended to 5 from 4 for SHA3-384, and to 6 from 5 for \(\texttt {SHAKE256}\).

Supported by the National Natural Science Foundation of China (Grant No. 62122085 and 12231015), the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDB0690000 and the Youth Innovation Promotion Association of Chinese Academy of Sciences.

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Acknowledgements

We thank the anonymous reviewers from CRYPTO 2024 for their thoughtful comments and insightful questions. Inspired by the reviewers, we were able to optimize the collision attack on 5-round SHA3-384.

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Correspondence to Meicheng Liu .

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Zhang, Z., Hou, C., Liu, M. (2024). Probabilistic Linearization: Internal Differential Collisions in up to 6 Rounds of SHA-3. In: Reyzin, L., Stebila, D. (eds) Advances in Cryptology – CRYPTO 2024. CRYPTO 2024. Lecture Notes in Computer Science, vol 14923. Springer, Cham. https://doi.org/10.1007/978-3-031-68385-5_8

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