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Find NIST statistical tests suite for (p)rng provided by National Institute of Science and Technology (Formerly NBS, National Bureau of Standards) here http://csrc.nist.gov/groups/ST/toolkit/rng/index.html and documention at NIST (see above) or here: National Institute of Science and Technology (Formerly NBS, National Bureau of Standards)


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I understand your question to be related with stream encoding by XORing a source using the bytes emitted from two by XORed PRNG. (By the way, you don't need to combine them before XOR the source - the result remains the same, if you XOR the source by bytes from both PRNGs in the same sequence.) I guess, you want to be able to decipher the result back to the ...


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Yes, you are reading this right. The requests for random value from NIST 800-90 drbgs perturbed the state. If this is a problem you can add a layer that optionally buffers values and always makes constant size requests.


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You can never actually prove that it was generated randomly or pseudorandomly. You can only prove with high probability that it wasn't. Calculating the number of heads and tails is one way. Another is calculating runs of consecutive heads or tails. There is a suite of statistical tests from NIST in their FIPS 140-2 document which is a good place to start. ...


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If I know the initial state and the time window that the first player pressed the button, I can just run the LFSR up to and through that time window. The first player's token will match one of these states, which means you know the LFSR state after player 1 has gone. You can repeat this to find the token for player 2, since applying the inversion gives you ...


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Here is an attack that I think will, with excellent odds, allow certain determination of which value is on the ticket of colluding players; and consequently give an advantage on guessing the tickets of these colluding players, and a (typically lesser) advantage on guessing the tickets of honest players. I'm assuming: The adversary knows the value on every ...


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I've read that a good RNG will have a range of p-values that follows a uniform distribution; values between 0 and 1 should happen with about equal probability. Why should that be so? It comes straight from the definition of p-values. The p-value indicates the probability you'd get at least that skewed a result if the source is truly random. So you ...



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