FDTA: A Value-Guaranteed Fair Data Trading Approach With a Three-Stage Stackelberg Game

With the merging of the Internet of Things and artificial intelligence, the data becomes valuable stuff and can be traded between utilities. Unfortunately, low-value data and conflicting interests among participants hinder the progress of data trading. To facilitate trading, assessing the data value and balancing the interests of each party gained widespread attention. In this article, we propose a value-guaranteed data trading approach using a three-stage Stackelberg game (SG), i.e., fair data trading approach (FDTA). Specifically, a consignment contract is negotiated between the trusted platform and data providers, which helps to release the cost of sale for both parties. A data-value quantification approach is developed based on the data size, completeness, and usability. Combined with the consignment contract, the data value is transparent to all parties before trading. Furthermore, a three-stage SG model is constructed to simulate the interactions between the trusted platform, data provider, and data consumer regarding data value. The trusted platform and data provider are the leaders, while the consumer is the follower. Besides, we prove that there is a unique Nash equilibrium in this game, ensuring that the interests of all participants are balanced. Finally, we conduct extensive experiments to evaluate the performance of FDTA. The results show that FDTA can effectively guarantee the effectiveness of data-value assessment and the fairness of trading.