|
Abstract:
|
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 data on employees of the U. S. federal government. The application includes the development and implementation of differentially private algorithms for synthesis and verification.
|
