Functional compression through graph coloring

Motivated by applications to sensor networks and privacy preserving databases, we consider the problem of functional compression. The objective is to separately compress possibly correlated discrete sources such that an arbitrary but fixed deterministic function of those sources can be computed given the compressed data from each source. We consider both the lossless and lossy computation of a function. Specifically, we present results of the rate regions for three instances of the problem where there are two sources: 1) lossless computation where one source is available at the decoder; 2) under a special condition, lossless computation where both sources are separately encoded; and 3) lossy computation where one source is available at the decoder. For all of these instances, we present a layered architecture for distributed coding: first pre-process data at each source using colorings of certain characteristic graphs and then use standard distributed source coding (a la Slepian and Wo...

• Type: Journal
• Year: 2010
• Where: TIT
• Authors: Vishal Doshi, Devavrat Shah, Muriel Médard, Michelle Effros
• Keywords: Education, Fixed Deterministic Function, Lossless Computation, Lossy Computation, TIT 2010