Toward Decentralized Fair Data Trading Based on Blockchain

Abstract

Various emerging applications and services such as smartphones, wearable devices, and Inter-net of Things (IoT) have brought great convenience to people's daily life, while producing huge amounts of data. Mass data have become a valuable asset, which creates a new business pattern called data trading. However, fairness becomes a challenge when conducting online data trading between participants who are not fully trusted by each other. That is, if a data seller sends the data before being paid, a data buyer might obtain the data without paying, and conversely if a data buyer pays before receiving the data, the data seller might not send the data contractually. Traditionally, a trusted third party is usually employed to settle the disputes between buyers and sellers, but this centralized party is vulnerable to the single-point-of-failure issue. Fortunately, Blockchain provides an approach to realizing data trading without centralized trusted third parties. In this article, we introduce Fairtrade, a decentralized fair data trading framework, to solve the challenges of data availability and trading fairness in decentralized data trading. We propose two different models with potential instantiations. In the first solution, we take advantage of homomorphic encryption and data sample techniques to improve the reliability of the system, and further guarantee the availability of the data during data trading. In the second solution, we integrate double-authentication-preventing signatures with smart contracts to achieve fairness during data trading. We also evaluate Fairtrade by implementing both frameworks, in which we test the time consumption of the main algorithms and also test the gas cost of the functions in the smart contracts on the blockchain. The experimental results show the practicality of the proposal.