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Truthful Linear Regression

Cummings, Rachel and Ioannidis, Stratis and Ligett, Katrina (2015) Truthful Linear Regression. . (Unpublished)

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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 truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential 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 overcome this challenge through an appropriate design of the computation and payment scheme.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Ligett, Katrina0000-0003-2780-6656
Additional Information:The first author was funded in part by NSF grant CNS-1254169, US-Israel Binational Science Foundation grant 2012348, and a Google Faculty Research Award. The third author was funded in part by NSF grant CNS-1254169, US-Israel Binational Science Foundation grant 2012348, the Charles Lee Powell Foundation, a Google Faculty Research Award, an Okawa Foundation Research Grant, and a Microsoft Faculty Fellowship. Work completed in part while the first and second authors were at Technicolor Research Labs. We thank Jenn Wortman Vaughan for her comments on the final version of this paper.
Funding AgencyGrant Number
Binational Science Foundation (USA-Israel)2012348
Google Faculty Research AwardUNSPECIFIED
Charles Lee Powell FoundationUNSPECIFIED
Okawa FoundationUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Record Number:CaltechAUTHORS:20190627-150412956
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96799
Deposited By: Tony Diaz
Deposited On:27 Jun 2019 22:09
Last Modified:03 Oct 2019 21:25

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