DPFDT: Decentralized Privacy-preserving Fair Data Trading System
Xiangyu Li, Zhenfu Cao, Jiachen Shen, Xiaolei Dong · 2021 17th International Conference on Mobility, Sensing and Networking (MSN) · 2021
In this paper, we introduce DPFDT - a decentralized privacy-preserving fair data trading system using smart contracts. In this system, data as a digital commodity can be sold by the seller to the buyer at a certain price. We first design a conditional anonymous scheme based on block-chain and a fair trading protocol. Then we integrate them to form DPFDT to provide fairness, conditional anonymity, privacy-preserving, and decentralization. A conditional anonymous scheme means that the user’s operations are carried out through anonymous accounts, but when an anonymous account does some malicious behaviors, its real account will be traced. A protocol is said to be fair if and only if the buyer pays, and he will be guaranteed to receive the correct data. While a few fair exchange protocols based on smart contracts have been proposed, the DPFDT has the following advantages: (1) Adding privacy-preserving into the system, including the privacy of both parties’ identities in trading and the privacy of the buyers’ needs; (2) Authorizing buyers the right to specify the order. That is, sellers will be asked in the order specified by the buyer; (3) Introducing a paid trial stage in the trading process, the loss of paying for unwanted goods will be minimized. Besides, security analysis shows that the proposed scheme has transaction security and trade security. Finally, experiments are done on the Ethernet platform. It shows that the DPFDT is efficient and practical in real-life applications.