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Only for the sake of simplicity: a good statistical PRNG can have no explicit seed or a very small seed space (e.g.16 bit). This is clearly not enough for crytographic purposes. The key point is that statistical PRNG doesnt need to be unpredictable, crypto PRNGs need it


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The key element in the definition of a PRG is the observer (aka distinguisher, algorithm, test, etc) that the PRG is supposed to fool. A statistical PRG fools a specific set of observers, whereas a cryptographic PRG fools all efficient observers. This strong definition is essential for cryptography:: The only assumption the designer should make about the ...


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A statistical PRNG is intended to not exhibit any statistical abnormalities. That is, an "adversary" who applies statistical analysis to the generated output should not be able to see a significant difference to the properties one expects from a uniformly distributed random source. For performance reasons, most statistical PRNGs are based on simple ...


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The key difference between the two is that a random number generator used for cryptographic purposes has to stand up to an attacker. When you use random numbers in statistics, the main thing you care about is that the output sequence "looks random." What that means in practice is that it passes a bunch of statistical tests, showing that the distribution of ...


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No, of course this is not a good idea. CLCG's were not designed for cryptographic purposes, and there's no reason to expect them to provide cryptographic security. Why would you do that, when there are perfectly good cryptographic-strength PRNGs available? As one simple example, if you use a CLCG built out of two linear congruential generators with the ...



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