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41st Annual Symposium on Foundations of Computer Science
Extracting randomness from samplable distributions
Redondo Beach, California
November 12November 14
ISBN: 0769508502
ASCII Text  x  
L. Trevisan, S. Vadhan, "Extracting randomness from samplable distributions," 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 32, 41st Annual Symposium on Foundations of Computer Science, 2000.  
BibTex  x  
@article{ 10.1109/SFCS.2000.892063, author = {L. Trevisan and S. Vadhan}, title = {Extracting randomness from samplable distributions}, journal ={2013 IEEE 54th Annual Symposium on Foundations of Computer Science}, volume = {0}, year = {2000}, issn = {02725428}, pages = {32}, doi = {http://doi.ieeecomputersociety.org/10.1109/SFCS.2000.892063}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  CONF JO  2013 IEEE 54th Annual Symposium on Foundations of Computer Science TI  Extracting randomness from samplable distributions SN  02725428 SP EP A1  L. Trevisan, A1  S. Vadhan, PY  2000 KW  random processes; computational complexity; samplable distributions; randomness extractor; almost uniform distribution; deterministic extraction procedures; sampling algorithm; minentropy; complexity VL  0 JA  2013 IEEE 54th Annual Symposium on Foundations of Computer Science ER   
The standard notion of a randomness extractor is a procedure which converts any weak source of randomness into an almost uniform distribution. The conversion necessarily uses a small amount of pure randomness, which can be eliminated by complete enumeration in some, but not all, applications. 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 sources satisfying strong independence requirements. We look at sources which are samplable, i.e. can be generated by an efficient sampling algorithm. We seek an efficient deterministic procedure that, given a sample from any samplable distribution of sufficiently large minentropy, gives an almost uniformly distributed output. We explore the conditions under which such deterministic extractors exist. We observe that no deterministic extractor exists if the sampler is allowed to use more computational resources than the extractor. On the other hand, if the extractor is allowed (polynomially) more resources than the sampler, we show that deterministic extraction becomes possible. This is true unconditionally in the nonuniform setting (i.e., when the extractor can be computed by a small circuit), and (necessarily) relies on complexity assumptions in the uniform setting.
Index Terms:
random processes; computational complexity; samplable distributions; randomness extractor; almost uniform distribution; deterministic extraction procedures; sampling algorithm; minentropy; complexity
Citation:
L. Trevisan, S. Vadhan, "Extracting randomness from samplable distributions," focs, pp.32, 41st Annual Symposium on Foundations of Computer Science, 2000
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