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So far as I know, Windows does not (but you never know, it has an endless series of APIs so anything is possible). However, Intel hardware does. The Microsoft C/C++ compiler (and the Intel compiler) has intrinsics for obtaining a hardware generated random value. That value is obtained by running two circuits backwards so that are unstable. They generate a ...


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If the permutation will fit in memory then the previous answer is the best approach. If it won't work then you might consider a linear congruential generator (LCG) of the form y = A * x + C. There is a lot of theory there (Knuth is always a good place to start for this). If you want N elements then you need to design a and b relative to N to get the full ...


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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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What you're looking for is called a key derivation function, and more specifically a key stretching function. A key derivation function takes some variable-size material and turns it into a fixed-size key in a deterministic way, such that calling the same function on the same input yields the same key, and the original material cannot be reconstructed from ...



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