Entropy of pseudo-random-number generators

Entropy of pseudo-random-number generators.

Since the work of Phys. Rev. Lett. 69, 3382 (1992)]] some pseudo-random-number generators are known to yield wrong results in cluster Monte Carlo simulations. In this contribution the fundamental mechanism behind this failure is discussed. Almost all random-number generators calculate a new pseudo-random-number x(i) from preceding values, x(i) =f( x(i-1), x(i-2), ..., x(i-q) ). Failure of these...

Pseudo-Random Number Generators

The first option here is not easy. Some pieces of data of this kind exist, for example from the decay of radioactive elements, but generally generation is slow and you cannot get the quantity of random numbers you require for modern applications. The second option here is what I’ll talk about today. Algorithms of this type are called “PseudoRandom Number Generators” (PRNGs). Many bad algorithms...

Class of Trustworthy Pseudo-Random Number Generators

With the widespread use of communication technologies, cryptosystems are therefore critical to guarantee security over open networks as the Internet. Pseudo-random number generators (PRNGs) are fundamental in cryptosystems and information hiding schemes. One of the existing chaos-based PRNGs is using chaotic iterations schemes. In prior literature, the iterate function is just the vectorial boo...

Security Analysis of Pseudo-Random Number Generators

A pseudo-random number generator (PRNG) is a deterministic algorithm that produces numbers whose distribution is indistinguishable from uniform. A formal security model for PRNGs with input was proposed in 2005 by Barak and Halevi (BH). This model involves an internal state that is refreshed with a (potentially biased) external random source, and a cryptographic function that outputs random num...

On Quaternionic Pseudo-Random Number Generators