Extracting Randomness Using Few Independent Sources

  • Authors:
  • Boaz Barak;Russell Impagliazzo;Avi Wigderson

  • Affiliations:
  • Institute for Advanced Study;University of California at San Diego;Institute for Advanced Study

  • Venue:
  • FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
  • Year:
  • 2004

Quantified Score

Hi-index 0.06

Visualization

Abstract

In this work we give the first deterministic extractors from a constant number of weak sources whose entropy rate is less than 1/2. Specifically, for every 驴 0 we give an explicit construction for extracting randomness from a constant (depending polynomially on 1/驴) number of distributions over {0, 1}^n, each having min-entropy 驴n. These extractors output n bits, which are 2^{- n} close to uniform. This construction uses several results from additive number theory, and in particular a recent one by Bourgain, Katz and Tao [BKT03] and of Konyagin [Kon03]. We also consider the related problem of constructing randomness dispersers. For any constant output length m, our dispersers use a constant number of identical distributions, each with min-entropy 驴(log n) and outputs every possible m-bit string with positive probability. The main tool we use is a variant of the "stepping-up lemma" used in establishing lower bound on the Ramsey number for hypergraphs (Erdos and Hajnal, [GRS80]).