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Here we have $d \ll m$, i.e., we start with a little bit of good randomness, and we end up with a lot. That's why it's called a "seed": you need something small to get you started, but you end up with a giant beautiful oak tree of randomness. The other thing to know is that uniformly distributed bits are high-quality randomness, whereas bits with min-...


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I'm not sure if this is what you are looking for, but as I recall, there is a mathematical proof that AMLS (advanced multi-level strategy) is maximal. This document does not contain the proof, but an outline of it is on page 9: Coin Toss As a practical example, I seed AMLS with timer output to update a hashed accumulator used for seeding a reference PRNG, ...


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I came across this late. Don't know if this question still matters. I am posting this as an answer since it is too long for a comment. If n can be 1 but m is not too large (say, at most a small exponential in the min-entropy), a random function will be a good extractor with high probability. It will cost you a factor of something like log m in the min-...


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