An iterative auction mechanism for data trading
In the big data era, it is vital to allocate the vast amount of data to various users 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. In this paper, the data trading problem of a data market with multiple data owners, collectors and users is formulated and an iterative auction mechanism is proposed to coordinate the data trading. The proposed mechanism guilds the selfish data agents to trade data efficiently 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. Simulations as well as real data experiments validate the theoretical properties of the mechanism.