International Association
for Cryptologic Research

The analytical perspective employed in the study classifies the theoretical research on dependencies in differential characteristics into two types. By categorising all dependence representations from the value restrictions and the theory of quasidifferential trails, we pinpoint a specific set of nonlinear constraints, which we term linearised nonlinear constraints. We aim to establish a method that utilises value restrictions to identify these constraints, as the current method based on value restrictions is found to be lacking in this area. A linearisation method for searching linearised nonlinear constraints for a given differential characteristic is developed by leveraging linear dependencies between inputs and outputs of active S-boxes. Then, we propose a three-stage evaluation approach to more accurately evaluate differential characteristics with linearised nonlinear constraints. Four differential characteristics of GIFT-64 are analysed using the three-stage evaluation approach, and the exact right key spaces and remaining probabilities are given. According to our results, the right key spaces of the four differential characteristics do not cover the entire key space, and the remaining probabilities are not equivalent to the stated probabilities. Concerning GIFT-128, we find six differential characteristics subject to linearised nonlinear constraints. Besides, inconsistencies are detected in the linear and linearised nonlinear constraints in the characteristics of two differentials employed to initiate the most effective differential attack on GIFT-128. Based on these results, we strongly advise reassessing the differential attacks that rely on these distinguishers. An additional advantage of using the linearisation method and the three-stage evaluation approach is their ability to identify linear and nonlinear constraints in ciphers that utilise the Generalised Feistel Network (GFN). It leads to the first instantiations of linear and nonlinear constraints in the GFN cipher WARP.