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cs.CR2026

Analyzing Linear Layers in Related-Differential Cryptanalysis

Yogesh Kumar, Akshay Ankush Yadav, Susanta Samanta

In AES-like ciphers, diffusion layers are commonly instantiated using MDS matrices, since their optimal branch number yields strong diffusion guarantees and underpins classical res…

cs.CR2026

New Insights into Involutory and Orthogonal MDS Matrices

Yogesh Kumar, Susanta Samanta, Atul Gaur

MDS matrices play a critical role in the design of diffusion layers for block ciphers and hash functions due to their optimal branch number. Involutory and orthogonal MDS matrices…

cs.CR2024

A New Algorithm for Computing Branch Number of Non-Singular Matrices over Finite Fields

P. R. Mishra, Yogesh Kumar, Susanta Samanta +1

The notion of branch numbers of a linear transformation is crucial for both linear and differential cryptanalysis. The number of non-zero elements in a state difference or linear m…

cs.CR2024

Construction of all MDS and involutory MDS matrices

Yogesh Kumar, P. R. Mishra, Susanta Samanta +2

In this paper, we propose two algorithms for a hybrid construction of all MDS and involutory MDS matrices over a finite field , respectively. The prop…

cs.CR2024

A Systematic Construction Approach for All Involutory MDS Matrices

Yogesh Kumar, P. R. Mishra, Susanta Samanta +1

Maximum distance separable (MDS) matrices play a crucial role not only in coding theory but also in the design of block ciphers and hash functions. Of particular interest are invol…