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 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.

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

“…In particular, H örner and Skrzypacz [2016] study the design of mechanisms for selling data and Goldfarb and Tucker [2011], Bergemann and Bonatti [2015], Montes et al [2019], and Jagabathula et al [2020] study the improvements in resource allocation using personal information. The correlation among users' data and its impact on the price of data has been studied in Liao et al [2018], Fainmesser et al [2019], Acemoglu et al [2021], Ichihashi [2021], and Liao et al [2021].…”