An Online Truthful Auction for IoT Data Trading with Dynamic Data Owners

Data is an extremely import asset in modern scientific and commercial society. The life force behind powerful AI or ML algorithms is data, especially lots of data, which makes data trading significantly essential to unlocking the power of AI or ML. Data owners who offer personal data and data consumers who request data blocks negotiate with each other to make an agreement on trading prices via a big data trading platform; consequently both sides gain profit from data transactions. A great many existing studies have investigated to trade various kinds of data as well as to protect data privacy, or to construct a decentralized data trading platform due to untrustworthy participants. However, existing studies neglect an important characteristic, i.e., dynamics of both data owners and data requests in IoT data trading. To this end, we first construct an auction-based model to formulate the data trading process and then propose an truthful online data trading algorithm which not only resolves the problem of matching dynamic data owners and randomly generated data requests, but also determines the data trading price of each data block. The proposed algorithm achieves several good properties, such as constant competitive ratio for near-optimal social efficiency, incentive-compatibility, individual rationality of participants, via rigorous theoretical analysis and extensive simulations.