#True random number generator algorithm how to
#True random number generator algorithm series

(We actually can't prove mathematically that a CSPRNG exists, but there are CSPRNG built on cryptographic primitives such as AES and SHA-2, which are CSPRNG under the same hypothesis that make AES suitable to derive encryption algorithms from, and SHA-2 a cryptographic hash function.)

#True random number generator algorithm how to

We don't know how to distinguish the output of a CSPRNG from a truly random sequence: given part of the output of a CSPRNG, we don't know how to reconstruct the outputs we aren't given. There exist cryptographically secure pseudorandom number generators, that is, random number generators which are deterministic but nonetheless indistinguishable from truly random sequences. (It is possible to verify that it's random enough for some applications such as Monte Carlo numerical methods, but not, say, to generate a cryptographic key.) It is impossible to verify that the output of a purported random generator is random enough for cryptography. There's also the BSI's AIS20/31 test suite, and the Dieharder test suite as well.

#True random number generator algorithm series

You can look at the NIST SP800-90A/B/C series for a good overview of RNG design, and especially SP800-90B (still draft) for entropy source evaluation.Д«SI also has guidelines in AIS20/31 which explain how to evaluate RNGs.ДЄnd finally, NIST's STS 800-22a is a good test suite, as already pointed out. RNG evaluation has been getting a lot more attention recently, and the standards are becoming more stringent. If the seed is coming from a true RNG, then 1-3 above apply.

And finally, for a DRNG that doesn't have a live entropy source, the initial seed quality needs to be evaluated.

If the RNG is not a true RNG, meaning that you're looking at a deterministic RNG, the DRNG algorithm implementation needs to be verified.

When you're running statistical tests on output produced by the said RNG, it needs to be done on data collected prior to the application of a conditioning/compression function to get a clear idea on the real amount of entropy you're working with.Physical or stochastic models are used for this. If there's a live noise source, you definitely need to check the implementation of the noise (entropy) source, and the underlying theory.You need to determine whether you're talking about a true RNG with a live entropy source, or a DRNG with an initial seed (or a combination of the two).There are several elements that are important in evaluating an RNG's randomness, unpredictability, or its entropy: So the tests can only point out that there is or isn't some inherent statistical weakness in the way the numbers were produced, they can't really confirm that they're unpredictable. Real 'randomness' comes down to the amount of entropy in the samples.ДЄnd entropy is more or less impossible to qualify in blackbox testing, as others pointed out.ДЄ good conditioning function and a simple input with very little information can pass statistical tests.