Deterministic Randomness Extraction From Generalized And Distributed Santha-Vazirani Sources

SIAM JOURNAL ON COMPUTING(2015)

引用 16|浏览25
暂无评分
摘要
A Santha-Vazirani (SV) source is a sequence of random bits where the conditional distribution of each bit, given the previous bits, can be partially controlled by an adversary. Santha and Vazirani show that deterministic randomness extraction from these sources is impossible. In this paper, we study the generalization of SV sources for nonbinary sequences. We show that unlike the binary case, deterministic randomness extraction in the generalized case is sometimes possible. We present a necessary condition and a sufficient condition for the possibility of deterministic randomness extraction. These two conditions coincide in " non-degenerate" cases.Next, we turn to a distributed setting. In this setting the SV source consists of a random sequence of pairs (a(1), b(1)), (a(2), b(2)),... distributed between two parties, where the first party receives a(i)'s and the second one receives bi's. The goal of the two parties is to extract common randomness without communication. Using the notion of maximal correlation, we prove a necessary condition and a sufficient condition for the possibility of common randomness extraction from these sources. Based on these two conditions, the problem of common randomness extraction essentially reduces to the problem of randomness extraction from (non-distributed) SV sources. This result generalizes results of Gacs and Korner, and Witsenhausen about common randomness extraction from i.i.d. sources to adversarial sources.
更多
查看译文
关键词
Common Data, Common Part, Annual IEEE Symposium, Total Variation Distance, Common Randomness
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要