  Federal administrative data, such as tax data, are invaluable for research,
but because of privacy concerns, access to these data is typically limited to
select agencies and a few individuals. An alternative to sharing microlevel
data is to allow individuals to query statistics without directly accessing the
confidential data. This paper studies the feasibility of using differentially
private (DP) methods to make certain queries while preserving privacy. We also
include new methodological adaptations to existing DP regression methods for
using new data types and returning standard error estimates. We define
feasibility as the impact of DP methods on analyses for making public policy
decisions and the queries accuracy according to several utility metrics. We
evaluate the methods using Internal Revenue Service data and public-use Current
Population Survey data and identify how specific data features might challenge
some of these methods. Our findings show that DP methods are feasible for
simple, univariate statistics but struggle to produce accurate regression
estimates and confidence intervals. To the best of our knowledge, this is the
first comprehensive statistical study of DP regression methodology on real,
complex datasets, and the findings have significant implications for the
direction of a growing research field and public policy.
