Almost Perfect Nonlinear (APN) Functions

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Background and definition

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

The formal definition of an APN function is usually given through the values

which, for , express the number of input pairs with difference that map to a given . The existence of a pair with a high value of makes the function vulnerable to differential cryptanalysis. This motivates the definition of differential uniformity as

which clearly satisfies for any function . The functions meeting this lower bound are called almost perfect nonlinear (APN).


Walsh transform[1]

Any -function satisfies

with equality characterizing APN functions.

Autocorrelation functions of the directional derivatives [2]

Given a Boolean function , the autocorrelation function of is defined as

Any -function satisfies

for any . Equality occurs if and only if is APN.

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

for .

Then any function satisfies

and equality occurs if and only if is APN.

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

  1. Florent Chabaud, Serge Vaudenay, Links between differential and linear cryptanalysis, Workshop on the Theory and Application of Cryptographic Techniques, 1994 May 9, pp. 356-365, Springer, Berlin, Heidelberg
  2. 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