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2000
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Extracting Randomness from Samplable Distributions

12 years 4 months ago
Extracting Randomness from Samplable Distributions
Randomness extractors convert weak sources of randomness into an almost uniform distribution; the conversion uses a small amount of pure randomness. In algorithmic applications, the use of extra randomness can be simulated by complete enumeration (alas, at the price of a considerable slow-down), but in other applications (e.g. in cryptography) the use of extra randomness is undesirable. In this paper, we consider the problem of deterministically converting a weak source of randomness into an almost uniform distribution. Previously, deterministic extraction procedures were known only for classes of distributions having strong independence requirement. Under complexity assumptions, we show how to extract randomness from any samplable distribution, i.e. a distribution that can be generated by an efficient sampling algorithm. Assuming that there are problems in E that are not solvable by subexponential-size circuits with 5 gates, we give a polynomial-time extractor that is able to transf...
Luca Trevisan, Salil P. Vadhan
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where FOCS
Authors Luca Trevisan, Salil P. Vadhan
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