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

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Andrew David Foote
Ashwin Machanavajjhala
Kevin McKinney


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).

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How to Cite
Foote, Andrew David, Ashwin Machanavajjhala, and Kevin McKinney. 2019. “Releasing Earnings Distributions Using Differential Privacy: Disclosure Avoidance System For Post-Secondary Employment Outcomes (PSEO)”. Journal of Privacy and Confidentiality 9 (2).
Author Biographies

Ashwin Machanavajjhala, Duke University

Department of Computer Science, Associate Professor

Kevin McKinney, U.S. Census Bureau

Center for Economic Studies, Senior Economist

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