Published June 27, 2019
| Submitted
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Truthful Linear Regression
Abstract
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.
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.Attached Files
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Additional details
- Eprint ID
- 96799
- Resolver ID
- CaltechAUTHORS:20190627-150412956
- CNS-1254169
- NSF
- 2012348
- Binational Science Foundation (USA-Israel)
- Google Faculty Research Award
- NSF
- Charles Lee Powell Foundation
- Okawa Foundation
- Microsoft Faculty Fellowship
- Created
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2019-06-27Created from EPrint's datestamp field
- Updated
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2023-06-02Created from EPrint's last_modified field