A Framework to Improve the Implementations of Linear Layers
Abstract: This paper presents a novel approach to optimizing the linear layer of block ciphers using the matrix decomposition framework. It is observed that the reduction properties proposed by Xiang et al. (in FSE 2020) need to be improved. To address these limitations, we propose a new reduction framework with a complete reduction algorithm and swapping algorithm. Our approach formulates matrix decomposition as a new framework with an adaptive objective function and converts the problem to a Graph Isomorphism problem (GI problem). Using the new reduction algorithm, we were able to achieve lower XOR counts and depths of quantum implementations under the s-XOR metric. Our results outperform previous works for many linear layers of block ciphers and hash functions; some of them are better than the current g-XOR implementation. For the AES MixColumn operation, we get two implementations with 91 XOR counts and depth 13 of in-place quantum implementation, respectively. https://tosc.iacr.org/index.php/ToSC/article/view/11633
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A Framework to Improve the Implementations of Linear Layers ; volume:2024 ; number:2 ; year:2024
IACR transactions on symmetric cryptology ; 2024, Heft 2 (2024)
- Urheber
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Yuan, Yufei
Wu, Wenling
Shi, Tairong
Zhang, Lei
Zhang, Yu
- DOI
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10.46586/tosc.v2024.i2.322-347
- URN
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urn:nbn:de:101:1-2407031854588.848251307011
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:46 MESZ
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Beteiligte
- Yuan, Yufei
- Wu, Wenling
- Shi, Tairong
- Zhang, Lei
- Zhang, Yu
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