Open Access
June 2018 Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government
Andrés F. Barrientos, Alexander Bolton, Tom Balmat, Jerome P. Reiter, John M. de Figueiredo, Ashwin Machanavajjhala, Yan Chen, Charley Kneifel, Mark DeLong
Ann. Appl. Stat. 12(2): 1124-1156 (June 2018). DOI: 10.1214/18-AOAS1194


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, that is, 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.


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Andrés F. Barrientos. Alexander Bolton. Tom Balmat. Jerome P. Reiter. John M. de Figueiredo. Ashwin Machanavajjhala. Yan Chen. Charley Kneifel. Mark DeLong. "Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government." Ann. Appl. Stat. 12 (2) 1124 - 1156, June 2018.


Received: 1 October 2017; Revised: 1 June 2018; Published: June 2018
First available in Project Euclid: 28 July 2018

zbMATH: 06980487
MathSciNet: MR3834297
Digital Object Identifier: 10.1214/18-AOAS1194

Keywords: Disclosure , privacy , public , remote , synthetic

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.12 • No. 2 • June 2018
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