The Software Institute of the Chinese Academy of Sciences has made progress in differential cryptanalysis of block encryption algorithms.

The Trusted Intelligent System Research Team of the Institute of Software, Chinese Academy of Sciences has made progress in differential cryptanalysis of block encryption algorithms . This work designed a domain programming language EasyBC for block encryption algorithms. On this basis, a universal and scalable differential cryptanalysis method was proposed, and a fully automatic analysis tool platform EasyBC was developed (Figure 1).

This research result was accepted by POPL 2024, the top international conference on programming languages, under the title EasyBC: A Cryptography-Specific Language for Security Analysis of Block Ciphers against Differential Cryptanalysis. The corresponding author is researcher Song Fu from the State Key Laboratory of Computer Science of the Institute of Software.

Figure 1. EasyBC platform flow chart

The block cipher algorithm divides plain text into multiple equal-length modules (blocks), and uses a symmetric key to encrypt or decrypt each group separately. It is widely used in many fields such as email encryption and bank transaction transfers. As an extremely important component of the encryption protocol, the mainstream block encryption algorithms include SM1, SM4 and SM7 promulgated by the State Cryptozoological Administration of China, and the standard algorithms AES and 3DES approved by the US government. Differential cryptanalysis plays a core role in evaluating the security of block encryption algorithms and is an indispensable security analysis method for the standardization of block encryption algorithms. The existing differential cryptanalysis methods have certain deficiencies in terms of versatility and automation. At the same time, the complexity of the modeling process requires users to be familiar with the application of a large number of modeling methods and underlying analysis tools.

In order to solve the above shortcomings, the research team designed a cryptography-specific high-level programming language EasyBC for block encryption algorithms, which provides a complete formal definition of syntax, type and semantics, laying a good foundation for automatic analysis of the security of block encryption algorithms; proposed Three differential cryptanalysis methods with different analysis accuracy and performance are proposed. It not only unifies and optimizes the existing modeling methods of various encryption operations, but also proposes a variety of new modeling methods.

The research team implemented 23 encryption primitives, including the underlying substitution algorithm of the National Institute of Standards and Technology (NIST) certified encryption scheme and a variety of commonly used block encryption algorithms (Figure 2); and The block cipher primitives were security analyzed (Figure 3), which verified the expressive ability of the EasyBC language and the effectiveness of the automatic security analysis of the EasyBC tool platform.

Figure 2. 23 encryption primitives implemented in EasyBC language

Figure 3. Security analysis results of differential cryptography of encryption primitives implemented by Word-wise

This research is of great significance to the study of differential cryptanalysis of block encryption algorithms, and provides a good research foundation and basis for subsequent cryptography-related researchers to conduct fully automatic security analysis of block encryption algorithms and performance evaluation of various computational operation modeling methods. Platform support.

Paper information:

EasyBC: A Cryptography-Specific Language for Security Analysis of Block Ciphers against Differential Cryptanalysis. Pu Sun (ShanghaiTech University), Fu Song* (Institute of Software Chinese Academy of Sciences, and University of Chinese Academy of Sciences), Yuqi Chen (ShanghaiTech University), Taolue Chen (Birkbeck, University of London). Proc. ACM Program. Lang. 8, POPL, Article 29 (January 2024), 33 pages. https://doi.org/10.1145/3632871

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