Protecting Data Markets from Strategic Buyers

The growing adoption of data analytics platforms and machine learning-based solutions for decision-makers creates a signicant demand for datasets, which explains the appearance of data markets. In a well-functioning data market, sellers share data in exchange for money, and buyers pay for datasets that help them solve problems. The market raises sucient money to compensate sellers and incentivize them to keep sharing datasets. This low-friction matching of sellers and buyers distributes the value of data among participants. But designing online data markets is challenging because they must account for the strategic behavior of participants.

In this paper, we introduce techniques to protect data markets from strategic participants, even when the asset traded is data. We combine those techniques into a pricing algorithm specically designed to trade data. The evaluation includes a user study and extensive simulations. Together, the evaluation demonstrates how participants strategize and the eectiveness of our techniques.