RELEASING EARNINGS DISTRIBUTIONS USING DIFFERENTIAL PRIVACY: DISCLOSURE AVOIDANCE SYSTEM FOR POST-SECONDARY EMPLOYMENT OUTCOMES (PSEO)

The U.S. Census Bureau recently released data on earnings percentiles of grad-uates 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 geometric noise 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, Raskhodnikova and Smith (2007).

Duke Scholars

Author Ashwinkumar Venkatanaga Machanavajjhala Computer Science