Almost Perfect Nonlinear (APN) Functions

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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 Ξ΄ and computing the output of F for all pairs of inputs (x1,x2) whose difference is Ξ΄ produces output pairs with a difference distribution that is far away from uniform.

The formal definition of an APN function F:𝔽2n→𝔽2n is usually given through the values

Ξ”F(a,b)=|{xβˆˆπ”½2n:F(x)+F(a+x)=b}|

which, for aβ‰ 0, express the number of input pairs with difference a that map to a given b. The existence of a pair (a,b)βˆˆπ”½2nβˆ—Γ—π”½2n with a high value of Ξ”F(a,b) makes the function F vulnerable to differential cryptanalysis. This motivates the definition of differential uniformity as

Ξ”F=max⁡{Ξ”F(a,b):aβˆˆπ”½2nβˆ—,bβˆˆπ”½2n}

which clearly satisfies Ξ”Fβ‰₯2 for any function F. The functions meeting this lower bound are called almost perfect nonlinear (APN).

Characterizations

Autocorrelation functions of the directional derivatives [1]

Given a Boolean function f:𝔽2n→𝔽2, the autocorrelation function of f is defined as

β„±(f)=βˆ‘xβˆˆπ”½2n(βˆ’1)f(x)=2nβˆ’2wt(f).

Any (n,n)-function F satisfies

βˆ‘Ξ»βˆˆπ”½2nβ„±(DafΞ»)=22n+1

for any aβˆˆπ”½2nβˆ—. Equality occurs if and only if F is APN.

This allows APN functions to be characterized in terms of the sum-of-square-indicator Ξ½(f) defined as

Ξ½(f)=βˆ‘aβˆˆπ”½2nβ„±2(DaF)=2βˆ’nβˆ‘aβˆˆπ”½2nβ„±4(f+Ο†a)

for Ο†a(x)=Tr(ax).

Then any (n,n) function F satisfies

βˆ‘Ξ»βˆˆπ”½2nβˆ—Ξ½(fΞ»)β‰₯(2nβˆ’1)22n+1

and equality occurs if and only if F is APN.

Similar techniques can be used to characterize permutations and APN functions with plateaued components.

  1. ↑ Thierry Berger, Anne Canteaut, Pascale Charpin, Yann Laigle-Chapuy, On Almost Perfect Nonlinear Functions Over GF(2^n), IEEE Transactions on Information Theory, 2006 Sep,52(9),4160-70