Measures of Anonymity

Abstract

To design a privacy-preserving data publishing system, we must first quantify the very notion of privacy, or information loss. In the past few years, there has been a proliferation of measures of privacy, some based on statistical considera- tions, others based on Bayesian or information-theoretic notions of information, and even others designed around the limitations of bounded adversaries. In this chapter, we review the various approaches to capturing privacy. We will find that although one can define privacy from different standpoints, there are many structural similarities in the way different approaches have evolved. It will also become clear that the notions of privacy and utility (the useful information one can extract from published data) are intertwined in ways that are yet to be fully resolved.