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In the asymptotic setting, a family of deterministic polynomial time computable functions : {,} {,} for some polynomial p, is a pseudorandom number generator (PRNG, or PRG in some references), if it stretches the length of its input (() > for any k), and if its output is computationally indistinguishable from true randomness, i.e. for any probabilistic polynomial time algorithm A, which ...
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...
Mask generation functions are deterministic; the octet string output is completely determined by the input octet string. The output of a mask generation function should be pseudorandom, that is, if the seed to the function is unknown, it should be infeasible to distinguish the output from a truly random string. [1]
Whether it is a console or a graphical interface application, the program must have an entry point of some sort. The entry point of a C# application is the Main method. There can only be one declaration of this method, and it is a static method in a class. It usually returns void and is passed command-line arguments as an array of strings.
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.
In practice, a salt is usually generated using a Cryptographically Secure PseudoRandom Number Generator. CSPRNGs are designed to produce unpredictable random numbers which can be alphanumeric. While generally discouraged due to lower security, some systems use timestamps or simple counters as a source of salt.
Random number generation in kernel space was implemented for the first time for Linux [2] in 1994 by Theodore Ts'o. [6] The implementation used secure hashes rather than ciphers, [clarification needed] to avoid cryptography export restrictions that were in place when the generator was originally designed.