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Improved Differential Cryptanalysis on SPECK Using Plaintext Structures | SpringerLink
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Improved Differential Cryptanalysis on SPECK Using Plaintext Structures

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Information Security and Privacy (ACISP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13915))

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Abstract

Plaintext structures are a commonly-used technique for improving differential cryptanalysis. Generally, there are two types of plaintext structures: multiple-differential structures and truncated-differential structures. Both types have been widely used in cryptanalysis of S-box-based ciphers while for SPECK, an Addition-Rotation-XOR (ARX) cipher, the truncated-differential structure has not been used so far. In this paper, we investigate the properties of modular addition and propose a method to construct truncated-differential structures for SPECK. Moreover, we show that a combination of both types of structures is also possible for SPECK. For recovering the key of SPECK, we propose dedicated algorithms and apply them to various differential distinguishers, which helps to obtain a series of improved attacks on all variants of SPECK. The results show that the combination of both structures helps to improve the data and time complexity at the same time, as in the cryptanalysis of S-box-based ciphers.

Z. Feng and Y. Luo—These authors contributed equally to this work.

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Notes

  1. 1.

    This precomputed table takes a small memory of \(2^{3\times (2\times \min \{a,n_{\textsf{BIL}}\}-y)}\).

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Acknowledgement

The authors would like to thank anonymous reviewers for their helpful comments and suggestions. The work of this paper was supported by the National Natural Science Foundation of China (Grants 62022036, 62132008, 62202460).

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Feng, Z., Luo, Y., Wang, C., Yang, Q., Liu, Z., Song, L. (2023). Improved Differential Cryptanalysis on SPECK Using Plaintext Structures. In: Simpson, L., Rezazadeh Baee, M.A. (eds) Information Security and Privacy. ACISP 2023. Lecture Notes in Computer Science, vol 13915. Springer, Cham. https://doi.org/10.1007/978-3-031-35486-1_1

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