Truthful Linear Regression
published: Aug. 20, 2015, recorded: July 2015, views: 3279
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 consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to report their data to the analyst truthfully constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differentially privacy to model individuals' privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We manage to overcome this this challenge, through appropriate design of the computation and payment scheme.
Download slides: colt2015_cummings_linear_regression_01.pdf (228.4 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !