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The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition

Liao, Guocheng and Su, Yu and Ziani, Juba and Wierman, Adam and Huang, Jianwei (2021) The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition. In: Proceedings of the 22nd ACM Conference on Economics and Computation. Association for Computing Machinery , New York, NY, Art. No. 689. ISBN 978-1-4503-8554-1. https://resolver.caltech.edu/CaltechAUTHORS:20210817-154521920

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Abstract

While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive compatible mechanism that optimizes the worst-case trade-off between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3465456.3467595DOIArticle
https://arxiv.org/abs/2105.14262arXivDiscussion Paper
Additional Information:© 2021 Copyright held by the owner/author(s).
Funders:
Funding AgencyGrant Number
Chinese University of Hong KongUNSPECIFIED
NSFCCF-1637598
NSFCNS-1518941
NSFCCF-1763307
University of PennsylvaniaUNSPECIFIED
Shenzhen Institute of Artificial Intelligence and Robotics for SocietyUNSPECIFIED
PIMCOUNSPECIFIED
Subject Keywords:privacy paradox; mechanism design; data correlation; online platform
DOI:10.1145/3465456.3467595
Record Number:CaltechAUTHORS:20210817-154521920
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210817-154521920
Official Citation:Guocheng Liao, Yu Su, Juba Ziani, Adam Wierman, and Jianwei Huang. 2021. The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition. In Proceedings of the 22nd ACM Conference on Economics and Computation (EC ’21), July 18–23, 2021, Budapest, Hungary. ACM, New York, NY, USA, 1 page. https: //doi.org/10.1145/3465456.3467595
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110288
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Aug 2021 22:00
Last Modified:18 Aug 2021 22:00

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