No Time to Hash: On Super-Efficient Entropy Accumulation
Yevgeniy Dodis, Siyao Guo, Noah Stephens-Davidowitz, Zhiye Xie
Abstract
Real-world random number generators (RNGs) cannot afford to use (slow) cryptographic hashing every time they refresh their state with a new entropic input . Instead, they use ``superefficient'' simple entropy-accumulation procedures, such as where rotates an -bit state by some fixed number . For example, Microsoft's RNG uses for and for . Where do these numbers come from? Are they good choices? Should rotation be replaced by a better permutation of the input bits?
In this work we initiate a rigorous study of these pragmatic questions, by modeling the sequence of successive entropic inputs as independent (but otherwise adversarial) samples from some natural distribution family . Our contribution is as follows.
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We define -monotone distributions as a rich family that includes relevant real-world distributions (Gaussian, exponential, etc.), but avoids trivial impossibility results.
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For any with , we show that rotation accumulates bits of entropy from independent samples from any (unknown) -monotone distribution with entropy .
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However, we also show that some choices of perform much better than others for a given . E.g., we show is one of the best choices for ; in contrast, is good, but generally worse than , for .
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More generally, given a permutation and , we define a simple parameter, the covering number , and show that it characterizes the number of steps before the rule accumulates nearly bits of entropy from independent, -monotone samples of min-entropy each.
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We build a simple permutation , which achieves nearly optimal for all values of simultaneously, and experimentally validate that it compares favorably with all rotations .
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