TY - JOUR AU - Foote, Andrew David AU - Machanavajjhala, Ashwin AU - McKinney, Kevin PY - 2019/10/18 Y2 - 2024/03/29 TI - Releasing Earnings Distributions using Differential Privacy: Disclosure Avoidance System For Post-Secondary Employment Outcomes (PSEO) JF - Journal of Privacy and Confidentiality JA - JPC VL - 9 IS - 2 SE - Articles DO - 10.29012/jpc.722 UR - https://journalprivacyconfidentiality.org/index.php/jpc/article/view/722 SP - AB - <p>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).</p> ER -