An Auction-based Pricing Model for Big Data Trading

With the rapid development of big data industry, considerable attention has been paid to data trading in the big data markets. However, the problems of optimal pricing and trading data effectively between data owners and data users are far from well-studied. In this paper, we propose a pricing model based on auction mechanisms. The model provides a new approach to enable data trading efficiently and fairly. Furthermore, this model protects the process of auction from being manipulated by a new type of fraud called false-name bidding attacks, where attackers could use multiple anonymous identities to improve their utilities. The experimental results on thousands of simulated transactions show that our model achieves satisfying performance in terms of social surplus, which is the total utility of data owners and data users.