Interactive communication: balanced distributions, correlated files, and average-case complexity

Abstract

Suppose (X,Y) is a pair of random variables distributed over a support set S. Person P/sub x/ knows X, person P/sub y/ knows Y, and both know S. Using a predetermined protocol, they exchange binary messages in order for P/sub y/ to learn X. P/sub x/ may or may not learn Y. Bounds on communication complexity are obtained and used to obtain efficient protocols for the correlated files problem where X and Y are binary strings (files) within a small edit distance from each other. The average number of bits required for P/sub y/ to learn X when at most m messages are permitted is also determined.<>