Truthful Linear Regression

author: Rachel Cummings, Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech)
published: Aug. 20, 2015,   recorded: July 2015,   views: 3283


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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.

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