Measuring Risk and Utility of Anonymized Data Using Information Theory

Before releasing anonymized microdata (individual data) it is essential to evaluate whether: i) their utility is high enough for their release to make sense; ii) the risk that the anonymized data result in disclosure of respondent identity or respondent attribute values is low enough. Utility and disclosure risk measures are used for the above evaluation, which normally lack a common theoretical framework allowing to trade off utility and risk in a consistent way. We explore in this paper the use of information-theoretic measures based on the notion of mutual information.