Releasing Earnings Distributions using Differential Privacy Disclosure Avoidance System For Post-Secondary Employment Outcomes (PSEO)

Main Article Content

Andrew David Foote
https://orcid.org/0000-0001-9041-7296
Ashwin Machanavajjhala
https://orcid.org/0000-0003-1555-7330
Kevin McKinney

Abstract

The U.S. Census Bureau recently released data on earnings percentiles of graduates from post-secondary institutions. This paper describes and evaluates the disclosure avoidance system developed for these statistics. We propose a differentially private algorithm for releasing these data based on standard differentially private building blocks, by constructing a histogram of earnings and the application of the Laplace mechanism to recover a differentially-private CDF of earnings. We demonstrate that our algorithm can release earnings distributions with low error, and our algorithm out-performs prior work based on the concept of smooth sensitivity from Nissim et al. (2007).

Article Details

How to Cite
Foote, Andrew, Ashwin Machanavajjhala, and Kevin McKinney. 2019. “Releasing Earnings Distributions Using Differential Privacy”. Journal of Privacy and Confidentiality 9 (2). https://doi.org/10.29012/jpc.722.
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Articles
Author Biographies

Ashwin Machanavajjhala, Duke University

Department of Computer Science, Associate Professor

Kevin McKinney, U.S. Census Bureau

Center for Economic Studies, Senior Economist