Paper 2021/1105

Improved Linear Approximations of SNOW-V and SNOW-Vi

Zhen Shi and Chenhui Jin and Yu Jin

Abstract

Abstract. in this paper, we improve the results of linear approximation of SNOW-V and SNOW-Vi.We optimized the automatic search program by replacing the S-box part with accurate characterizations of the Walsh spectral of S-boxes, which results in a series of trails with higher correlations. On the basis of existing results, we investigate the common features of linear approximation trails with high correlation, and search for more trails by exhausting free masks. By summing up the correlations of trails with the same input and output masks, we get closer to the real correlation. As a result, we get a linear approximation with a correlation -2^{-47.76},which results in a correlation attack on SNOW-V and SNOW-Vi with a time complexity 2^{246:53}, data complexity 2^{237.5} and memory complexity 2^{238.77}.