MDB: An Evaluative and Incentivizing Model Trading Market

Existing data trading markets trade source data, which inevitably raises the issue of data ownership, and more recently model trading markets, which trade on machine learning models that guarantee the ownership of the data owner, have become increasingly popular. However, there are still difficulties in the assessment of the utility of the model and the distribution of market benefits. In this paper, we design a framework for a model trading market (MDB) that considers the interaction between data providers and broker. We evaluate the utility of the model in a variety of ways, including data quality, privacy protection needs, and the number of data providers, and reveal the dynamic decision-making process of data providers as they trade off the utility of privacy against the utility of data compensation. At the same time, we analyze the optimal allocation compensation strategy of brokers to maximize the welfare of the whole market. The feasibility and effectiveness of the MDB model trading market are demonstrated by numerical results.