Differentially private publication of data on wages and job mobility

Article type: Research Article

Authors: Schmutte, Ian M.

Affiliations: Department of Economics, Terry College of Business, University of Georgia, Athens, Georgia. E-mail: schmutte@uga.edu

Abstract: Brazil, like many countries, is reluctant to publish business-level data, because of legitimate concerns about the establishments' confidentiality. A trusted data curator can increase the utility of data, while managing the risk to establishments, either by releasing synthetic data, or by infusing noise into published statistics. This paper evaluates the application of a differentially private mechanism to publish statistics on wages and job mobility computed from Brazilian employer-employee matched data. The publication mechanism can result in both the publication of specific statistics as well as the generation of synthetic data. I find that the tradeoff between the privacy guaranteed to individuals in the data, and the accuracy of published statistics, is potentially much better that the worst-case theoretical accuracy guarantee. However, the synthetic data fare quite poorly in analyses that are outside the set of queries to which it was trained. Note that this article only explores and characterizes the feasibility of these publication strategies, and will not directly result in the publication of any data.

Keywords: Demand for public statistics, technology for statistical agencies, optimal data accuracy, optimal confidentiality protection, matched employer-employee data, job mobility, differential privacy

DOI: 10.3233/SJI-160962

Journal: Statistical Journal of the IAOS, vol. 32, no. 1, pp. 81-92, 2016