How do we measure privacy?

Abstract

We survey the state of the art of privacy in perturbative methods for statistical disclosure control. While the focus is on data microaggregation, these methods also address a wide variety of alternative applications such as obfuscation in location-based services. More specifically, we examine – anonymity and some of its enhancements. Motivated by the vulnerability of these measures to similarity and skewness attacks, we compare three recent criteria for privacy based on information-theoretic concepts that attempt to circumvent this vulnerability.