Increasing the output length of zero-error dispersers

Ariel Gabizon, Ronen Shaltiel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Let be a class of probability distributions over a finite set Ω. A function D : Ω → {0,1}m is a disperser for with entropy threshold k and error if for any distribution X in such that X gives positive probability to at least 2 k elements we have that the distribution D(X) gives positive probability to at least (1 - ε)2m elements. A long line of research is devoted to giving explicit (that is polynomial time computable) dispersers (and related objects called "extractors") for various classes of distributions while trying to maximize m as a function of k. In this paper we are interested in explicitly constructing zero-error dispersers (that is dispersers with error ε = 0). For several interesting classes of distributions there are explicit constructions in the literature of zero-error dispersers with "small" output length m and we give improved constructions that achieve "large" output length, namely m = Ω(k). We achieve this by developing a general technique to improve the output length of zero-error dispersers (namely, to transform a disperser with short output length into one with large output length). This strategy works for several classes of sources and is inspired by a transformation that improves the output length of extractors (which was given in [29] building on earlier work by [15]). Nevertheless, we stress that our techniques are different than those of [29] and in particular give non-trivial results in the errorless case. Using our approach we construct improved zero-error dispersers for the class of 2-sources. More precisely, we show that for any constant δ > 0 there is a constant η > 0 such that for sufficiently large n there is a poly-time computable function D : {0,1}n × {0,1}n → {0,1}ηnsuch that for any two independent distributions X 1, X2 over {0,1}n such that both of them support at least 2δn elements we get that the output distribution D(X1, X2) has full support. This improves the output length of previous constructions by [2] and has applications in Ramsey Theory and in constructing certain data structures [13]. We also use our techniques to give explicit constructions of zero-error dispersers for bit-fixing sources and affine sources over polynomially large fields. These constructions improve the best known explicit constructions due to [26,14] and achieve m = Ω(k) for bit-fixing sources and m = k - o(k) for affine sources.

Original languageEnglish
Title of host publicationApproximation, Randomization and Combinatorial Optimization
Subtitle of host publicationAlgorithms and Techniques - 11th International Workshop, APPROX 2008 and 12th International Workshop, RANDOM 2008, Proceedings
Pages430-443
Number of pages14
DOIs
StatePublished - 2008
Event11th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2008 and 12th International Workshop on Randomization and Computation, RANDOM 2008 - Boston, MA, United States
Duration: 25 Aug 200827 Aug 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5171 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2008 and 12th International Workshop on Randomization and Computation, RANDOM 2008
Country/TerritoryUnited States
CityBoston, MA
Period25/08/0827/08/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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