Statistical integral distinguisher with multi-structure and its application on AES-like ciphers
| Autor: | Tingting Cui, Ling Sun, Sihem Mesnager, Huaifeng Chen, Meiqin Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
Computer science Applied Mathematics Hash function Structure (category theory) Mode (statistics) 020206 networking & telecommunications Field (mathematics) 02 engineering and technology Data complexity Whirlpool Computational Theory and Mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Time complexity Algorithm |
| Zdroj: | Cryptography and Communications. 10:755-776 |
| ISSN: | 1936-2455 1936-2447 |
| Popis: | Integral attack is one of the most powerful tools in the field of symmetric ciphers. In order to reduce the time complexity of original integral one, Wang et al. firstly proposed a statistical integral distinguisher at FSE’16. However, they don’t consider the cases that there are several integral properties on output and multiple structures of data should be used at the same time. In terms of such cases, we put forward a new statistical integral distinguisher, which enables us to reduce the data complexity comparing to the traditional integral ones under multiple structures. As illustrations, we use it into the known-key distinguishers on AES-like ciphers including AES and the permutations of Whirlpool, PHOTON and Grostl-256 hash functions based on the Gilbert’s work at ASIACRYPT’14. These new distinguishers are the best ones comparing with previous ones under known-key setting. Moreover, we propose a secret-key distinguisher on 5-round AES under chosen-ciphertext mode. Its data, time and memory complexities are 2114.32 chosen ciphertexts, 2110 encryptions and 233.32 blocks. This is the best integral distinguisher on AES with secret S-box under secret-key setting so far. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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