Global Measures of Data Utility for Microdata Masked for Disclosure Limitation
Main Article Content
When releasing microdata to the public, data disseminators typically alter the original data to protect the confidentiality of database subjects' identities and sensitive attributes. However, such alteration negatively impacts the utility (quality) of the released data. In this paper, we present quantitative measures of data utility for masked microdata, with the aim of improving disseminators' evaluations of competing masking strategies. The measures, which are global in that they reflect similarities between the entire distributions of the original and released data, utilize empirical distribution estimation, cluster analysis, and propensity scores. We evaluate the measures using both simulated and genuine data. The results suggest that measures based on propensity score methods are the most promising for general use.
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National Science Foundation
Grant numbers EIA–013188