The Annals of Applied Statistics

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, and Mark DeLong

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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, 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.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 2 (2018), 1124-1156.

Dates
Received: October 2017
Revised: June 2018
First available in Project Euclid: 28 July 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1532743488

Digital Object Identifier
doi:10.1214/18-AOAS1194

Mathematical Reviews number (MathSciNet)
MR3834297

Keywords
Disclosure privacy public remote synthetic

Citation

Barrientos, Andrés F.; Bolton, Alexander; Balmat, Tom; Reiter, Jerome P.; de Figueiredo, John M.; Machanavajjhala, Ashwin; Chen, Yan; Kneifel, Charley; DeLong, Mark. 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 (2018), no. 2, 1124--1156. doi:10.1214/18-AOAS1194. https://projecteuclid.org/euclid.aoas/1532743488


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Supplemental materials

  • Supplement A to “Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government”. This document provides supporting material for aspects of the OPM synthesis plus verification application. In Section 1, we provide a formal description of the three submodels used to model the employee’s career. In Section 2, we discuss the modeling strategies used to synthesize variables in the OPM data. In Section 3, we provide the full list of the synthesized variables along with a brief description of each of them. In Section 4, we present the analyses of wage gaps conditional on six broad categories of occupation rather than the 803 used in the main text. In Section 5, we describe a method for empirical disclosure risk assessment for OPM synthetic data. In Section 6, we formally describe the verification measures for longitudinal trends in regression coefficients. In Section 7, we examine the performance of the $\varepsilon $-differentially private verification measures used in the text, and we present a verification measure that is suitable for analyses where some regression coefficients are nonestimable.
  • Supplement B to “Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government”. This document provides graphical analyses comparing the OPM synthetic and confidential data used in the main text.
  • Supplement C to “Providing access to confidential research data through synthesis and verification: An application to data on employees of the U.S. federal government”. This file contains the code used to generate the synthetic OPM data and compute the verification measures proposed in the main text.