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

TL;DR

This paper investigates the privacy paradox by analyzing how information leakage affects data sharing incentives and proposes an incentive-compatible mechanism to optimize bias-variance trade-offs in data acquisition under budget constraints.

Contribution

It introduces a novel mechanism design addressing privacy concerns and information leakage, providing a closed-form characterization of optimal data acquisition strategies.

Findings

Optimal mechanism balances bias and variance under privacy constraints

Characterizes the structure of optimal data acquisition mechanisms

Analyzes monotonicity properties of data marketplaces

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.