# Providing Access to Confidential Research Data Through Synthesis and Verification: An Application to Data on Employees of the U.S. Federal Government

## TL;DR

This paper demonstrates a method combining synthetic data generation and verification to provide privacy-protected access to confidential federal employee data, enabling reliable analysis of pay disparities across races.

## Contribution

It introduces a novel model for generating synthetic longitudinal career data and verification algorithms satisfying differential privacy, applied to federal employee data.

## Key findings

- The system allows exploration of pay differentials with privacy guarantees.
- Analysis revealed previously undocumented racial pay disparities.
- Verification helps identify which synthetic data inferences are reliable.

## 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 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.