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pseudorandom generator : ウィキペディア英語版
pseudorandom generator

In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution. The random seed is typically a short binary string drawn from the uniform distribution.
Many different classes of statistical tests have been considered in the literature, among them the class of all Boolean circuits of a given size.
It is not known whether good pseudorandom generators for this class exist, but it is known that their existence is in a certain sense equivalent to (unproven) circuit lower bounds in computational complexity theory.
Hence the construction of pseudorandom generators for the class of Boolean circuits of a given size rests on currently unproven hardness assumptions.
==Definition==
Let \mathcal A = \^
*\} be a class of functions.
These functions are the ''statistical tests'' that the pseudorandom generator will try to fool, and they are usually algorithms.
Sometimes the statistical tests are also called ''adversaries''.
A function G: \^\ell\to\^n with \ell \leq n is a ''pseudorandom generator'' against \mathcal A with ''bias'' \epsilon if, for every A in \mathcal A, the statistical distance between the distributions A(G(U_\ell)) and A(U_n) is at most \epsilon, where U_k is the uniform distribution on \^k.
The quantity \ell is called the ''seed length'' and the quantity n-\ell is called the ''stretch'' of the pseudorandom generator.
A pseudorandom generator against a family of adversaries (\mathcal A_n)_ with bias \epsilon(n) is a family of pseudorandom generators (G_n)_, where G_n : \^\to\^n is a pseudorandom generator against \mathcal A_n with bias \epsilon(n) and seed length \ell(n).
In most applications, the family \mathcal A represents some model of computation or some set of algorithms, and one is interested in designing a pseudorandom generator with small seed length and bias, and such that the output of the generator can be computed by the same sort of algorithm.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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