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Federal administrative data, such as tax data, are invaluable for research,but because of privacy concerns, access to these data is typically limited toselect agencies and a few individuals. An alternative to sharing microleveldata is to allow individuals to query statistics without directly accessing theconfidential data. This paper studies the feasibility of using differentiallyprivate (DP) methods to make certain queries while preserving privacy. We alsoinclude new methodological adaptations to existing DP regression methods forusing new data types and returning standard error estimates. We definefeasibility as the impact of DP methods on analyses for making public policydecisions and the queries accuracy according to several utility metrics. Weevaluate the methods using Internal Revenue Service data and public-use CurrentPopulation Survey data and identify how specific data features might challengesome of these methods. Our findings show that DP methods are feasible forsimple, univariate statistics but struggle to produce accurate regressionestimates and confidence intervals. To the best of our knowledge, this is thefirst comprehensive statistical study of DP regression methodology on real,complex datasets, and the findings have significant implications for thedirection of a growing research field and public policy.

A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data