Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 2021 | Published
Journal Article Open

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

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 extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff 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.

Additional Information

Copyright is held by author/owner(s). Online: 20 January 2022. Published: 20 January 2022.

Attached Files

Published - 3512798.3512802.pdf

Files

3512798.3512802.pdf
Files (853.3 kB)
Name Size Download all
md5:f49458e519a2f1ece5832658651fabb4
853.3 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
August 22, 2023