An Empirical Comparison of Some Methods for Disclosure Risk Assessment
Michael E. Carlson · 2002
With the release of public-use microdata filles it is important to assess the risk of disclosing individual information. A measure of disclosure risk often considered in the literature is the proportion of unique records in the file that are also unique in the population. Various methods based on superpopulation models have been proposed for estimating this quantity using sample data. An empirical comparison of a selection of models applied to three real-life data sets is presented. The general conclusion is that no one model is uniformly best with respect to the risk measure used and that performance varies greatly between di¤erent types of data.