Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism

In the big data era, it is vital to allocate the vast amount of data to heterogeneous users with different interests. To clinch this goal, various agents including data owners, collectors, and users should cooperate to trade data efficiently. However, the data agents (data owners, collectors, and users) are selfish and seek to maximize their own utilities instead of the overall system efficiency. As such, a sophisticated mechanism is imperative to guide the agents to distribute data efficiently. In this paper, the data trading problem of a datamarket with multiple data owners, collectors, and users is formulated and an iterative auctionmechanism is proposed to coordinate the trading. The proposed mechanism guides the selfish data agents to trade data efficiently in terms of social welfare and avoids direct access of the agents' private information. We theoretically prove that the proposed mechanism can achieve the socially optimal operation point. Moreover, we demonstrate that the mechanism satisfies appealing economic properties such as individual rationality and weakly balanced budget. Then, we expand the mechanism to nonexclusive data trading, in which the same data can be dispensed to multiple collectors and users. Simulations as well as real data experiments validate the theoretical properties of the mechanism.