Probabilistic Inference and Differential Privacy
published: March 25, 2011, recorded: December 2010, views: 3294
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We identify and investigate a strong connection between probabilistic inference and differential privacy, the latter being a recent privacy definition that permits only indirect observation of data through noisy measurement. Previous research on differential privacy has focused on designing measurement processes whose output is likely to be useful on its own. We consider the potential of applying probabilistic inference to the measurements and measurement process to derive posterior distributions over the data sets and model parameters thereof. We find that probabilistic inference can improve accuracy, integrate multiple observations, measure uncertainty, and even provide posterior distributions over quantities that were not directly measured.
Download slides: nips2010_mcsherry_pid_01.pdf (352.1 KB)
Download article: nips2010_1276.pdf (1.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !