Releasing Earnings Distributions using Differential Privacy

The Journal of Privacy and Confidentiality

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