An Incentive Mechanism for Trading Personal Data in Data Markets

TL;DR

This paper proposes a pricing mechanism for data markets that balances privacy and utility, incentivizing accurate privacy cost reporting and maximizing consumer profit while protecting personal data.

Contribution

It introduces a novel incentive-compatible pricing mechanism that accounts for privacy-utility trade-offs in data trading, with formal proofs and experimental validation.

Findings

Mechanism incentivizes truthful privacy cost reporting.

Balances privacy protection with data utility.

Maximizes data consumer profit within budget constraints.

Abstract

With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this trend, the model of data market trading between data providers and data consumers, is starting to emerge as a process to obtain high-quality personal information in exchange for some compensation. However, the risk of privacy violations caused by personal data analysis hinders data providers' participation in the data market. Differential privacy, a de-facto standard for privacy protection, can solve this problem, but, on the other hand, it deteriorates the data utility. In this paper, we introduce a pricing mechanism that takes into account the trade-off between privacy and accuracy. We propose a method to induce the data provider to accurately report her privacy price and, we optimize it in order to maximize the data consumer's profit within budget constraints. We show formally that the proposed mechanism achieves these properties, and also, validate them experimentally.