The Journal of Privacy and Confidentiality | |
Releasing Earnings Distributions using Differential Privacy | |
Ashwin Machanavajjhala1  Andrew David Foote2  Kevin McKinney2  | |
[1] Duke University;U.S. Census Bureau; | |
关键词: Differential Privacy; Education data; | |
DOI : 10.29012/jpc.722 | |
来源: DOAJ |
【 摘 要 】
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).
【 授权许可】
Unknown