Injecting Utility Into Anonymized Datasets

Abstract

Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be useful for studying the characteristics of a population. Privacy requirements such as <i>k</i>-anonymity and <i>l</i>-diversity are designed to thwart attacks that attempt to identify individuals in the data and to discover their sensitive information. On the other hand, the utility of such data has not been well-studied.In this paper we will discuss the shortcomings of current heuristic approaches to measuring utility and we will introduce a formal approach to measuring utility. Armed with this utility metric, we will show how to inject additional information into <i>k</i>-anonymous and <i>l</i>-diverse tables. This information has an intuitive semantic meaning, it increases the utility beyond what is possible in the original <i>k</i>-anonymity and <i>l</i>-diversity frameworks, and it maintains the privacy guarantees of <i>k</i>-anonymity and <i>l</i>-diversity.