CRDMITFeb 26, 2021

Recovering or Testing Extended-Affine Equivalence

arXiv:2103.00078v319 citations
Originality Incremental advance
AI Analysis

This work addresses a difficult problem in cryptography and coding theory, offering practical improvements for classifying quadratic APN functions, though it is incremental in nature.

The paper tackles the problem of Extended Affine (EA) equivalence for vectorial Boolean functions, presenting a new algorithm that efficiently solves EA-recovery for quadratic functions, outperforming all previous methods, and introduces a fast invariant for EA-partitioning that distinguishes between EA-inequivalent quadratic APN functions.

Extended Affine (EA) equivalence is the equivalence relation between two vectorial Boolean functions $F$ and $G$ such that there exist two affine permutations $A$, $B$, and an affine function $C$ satisfying $G = A \circ F \circ B + C$. While the problem has a simple formulation, it is very difficult in practice to test whether two functions are EA-equivalent. This problem has two variants: {\em EA-partitioning} deals with partitioning a set of functions into disjoint EA-equivalence classes, and \emph{EA-recovery} is about recovering the tuple $(A,B,C)$ if it exists. In this paper, we present a new algorithm that efficiently solves the EA-recovery problem for quadratic functions. Although its worst-case complexity occurs when dealing with APN functions, it supersedes, in terms of performance, all previously known algorithms for solving this problem for all quadratic functions and in any dimension, even in the case of APN functions. This approach is based on the Jacobian matrix of the functions, a tool whose study in this context can be of independent interest. The best approach for EA-partitioning in practice mainly relies on class invariants. We provide an overview of the known invariants along with a new one based on the \emph{ortho-derivative}. This new invariant is applicable to quadratic APN functions, a specific type of functions that is of great interest, and of which tens of thousands need to be sorted into distinct EA-classes. Our ortho-derivative-based invariant is very fast to compute, and it practically always distinguishes between EA-inequivalent quadratic APN functions.

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