Computational Extractors and Pseudorandomness

Dana Dachman-Soled, Rosario Gennaro, Hugo Krawczyk, Tal Malkin

Abstract: Computational extractors are efficient procedures that map a source of sufficiently high min-entropy to an output that is computationally indistinguishable from uniform. By relaxing the statistical closeness property of traditional randomness extractors one hopes to improve the efficiency and entropy parameters of these extractors, while keeping their utility for cryptographic applications. In this work we investigate computational extractors and consider questions of existence and inherent complexity from the theoretical and practical angles, with particular focus on the relationship to pseudorandomness.

External IDs:dblp:conf/tcc/Dachman-SoledGKM12