Concepedia

A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality

Alan F. Karr, Christine N. Kohnen, Anna Oganian, Jerome P. Reiter, Ashish Sanil

The American Statistician · 2006 · 195 citations · 26 references

TL;DR

When releasing data to the public, statistical agencies and survey organizations typically alter data values to protect the confidentiality of respondents’ identities and attribute values. The study aims to provide tools for selecting among data alteration methods by evaluating the utility of proposed releases through measures that compare inferences from altered and original data. The authors combine utility measures with disclosure risk metrics and apply them to both real and simulated datasets within a decision‑theoretic framework to assess risk‑utility tradeoffs. They demonstrate that these measures can be employed in a decision‑theoretic framework to evaluate disclosure‑limitation procedures using real and simulated data.

Abstract

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.