Utility and Privacy of Data Sources: Can Shannon Help Conceal and Reveal Information?

TL;DR

This paper proposes a rate distortion theory-based framework to quantify the privacy-utility tradeoff in large data repositories, addressing data leakage concerns and external knowledge effects.

Contribution

It introduces an analytical framework using rate distortion theory to measure privacy and utility, applicable across various data types and privacy methods.

Findings

Develops application-independent privacy and utility metrics

Models external side-information affecting privacy

Analyzes successive disclosures in data sources

Abstract

The problem of private information "leakage" (inadvertently or by malicious design) from the myriad large centralized searchable data repositories drives the need for an analytical framework that quantifies unequivocally how safe private data can be (privacy) while still providing useful benefit (utility) to multiple legitimate information consumers. Rate distortion theory is shown to be a natural choice to develop such a framework which includes the following: modeling of data sources, developing application independent utility and privacy metrics, quantifying utility-privacy tradeoffs irrespective of the type of data sources or the methods of providing privacy, developing a side-information model for dealing with questions of external knowledge, and studying a successive disclosure problem for multiple query data sources.