  Data stewards seeking to provide access to large-scale social science data
face a difficult challenge. They have to share data in ways that protect
privacy and confidentiality, are informative for many analyses and purposes,
and are relatively straightforward to use by data analysts. One approach
suggested in the literature is that data stewards generate and release
synthetic data, i.e., data simulated from statistical models, while also
providing users access to a verification server that allows them to assess the
quality of inferences from the synthetic data. We present an application of the
synthetic data plus verification server approach to longitudinal data on
employees of the U.S. federal government. As part of the application, we
present a novel model for generating synthetic career trajectories, as well as
strategies for generating high dimensional, longitudinal synthetic datasets. We
also present novel verification algorithms for regression coefficients that
satisfy differential privacy. We illustrate the integrated use of synthetic
data plus verification via analysis of differentials in pay by race. The
integrated system performs as intended, allowing users to explore the synthetic
data for potential pay differentials and learn through verifications which
findings in the synthetic data hold up and which do not. The analysis on the
confidential data reveals pay differentials across races not documented in
published studies.
