A framework for evaluating the utility of data altered to protect confidentiality

When releasing data to the public, statistical agencies and survey organizations typically alter data values in order to protect the confidentiality of survey respondents' identities and attribute values. To select among the wide variety of data alteration methods, agencies require tools for evaluating the utility of proposed data releases. Such utility measures can be combined with disclosure risk measures to gauge risk-utility tradeoffs of competing methods. This article presents utility measures focused on differences in inferences obtained from the altered data and corresponding inferences obtained from the original data. Using both genuine and simulated data, we show how the measures can be used in a decision-theoretic formulation for evaluating disclosure limitation procedures. © American Statisticial Association.

Duke Scholars

Author Jerome P. Reiter Statistical Science