Achievable Key Rates for Universal Simulation of Random Data With Respect to a Set of Statistical Tests

Abstract

We consider the problem of universal simulation of an unknown source from a certain parametric family of discrete memoryless sources, given a training vector X from that source and given a limited budget of purely random key bits. The goal is to generate a sequence of random vectors {Y/sub i/}, all of the same dimension and the same probability law as the given training vector X, such that a certain, prescribed set of M statistical tests will be satisfied. In particular, for each statistical test, it is required that for a certain event, /spl epsiv//sub /spl lscr//, 1 /spl les/ /spl lscr/ /spl les/ M, the relative frequency /sup 1///sub N/ /spl Sigma//sub i=1//sup N/ 1/sub /spl epsiv//spl lscr//(Y/sub i/) (1/sub /spl epsiv//(/spl middot/) being the indicator function of an event /spl epsiv/), would converge, as N /spl rarr/ /spl infin/, to a random variable (depending on X), that is typically as close as possible to the expectation of 1/sub /spl epsiv//spl lscr/,/ (X) with respect to the true unknown source, namely, to the probability of the event /spl epsiv//sub /spl lscr//. We characterize the minimum key rate needed for this purpose and demonstrate how this minimum can be approached in principle.