4 votes
Accepted

Motivation for randomness extractors

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 ...
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1 vote

Strong seeded randomness extractors with low entropy loss

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 ...
1 vote

deterministic randomness extractor and privacy

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 ...

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