Almost Perfect Nonlinear (APN) Functions

Background and definition

Almost perfect nonlinear (APN) functions are the class of $(n,n)$ Vectorial Boolean Functions that provide optimum resistance to against differential attack. Intuitively, the differential attack against a given cipher incorporating a vectorial Boolean function $F$ is efficient when fixing some difference $\delta$ and computing the output of $F$ for all pairs of inputs $(x_1,x_2)$ whose difference is $\delta$ produces output pairs with a difference distribution that is far away from uniform.

The formal definition of an APN function $F:{𝔽}_{2^n}\to {𝔽}_{2^n}$ is usually given through the values

${\Delta }_F(a,b)=|\{x\in {𝔽}_{2^n}:F(x)+F(a+x)=b\}|$

which, for $a\ne 0$, express the number of input pairs with difference $a$ that map to a given $b$. The existence of a pair $(a,b)\in {𝔽}_{2^n}^{\ast }\times {𝔽}_{2^n}$ with a high value of ${\Delta }_F(a,b)$ makes the function $F$ vulnerable to differential cryptanalysis. This motivates the definition of differential uniformity as

${\Delta }_F=max\{{\Delta }_F(a,b):a\in {𝔽}_{2^n}^{\ast },b\in {𝔽}_{2^n}\}$

which clearly satisfies ${\Delta }_F\ge 2$ for any function $F$. The functions meeting this lower bound are called almost perfect nonlinear (APN).