Secure and Fair Data Trading Based on Blockchain With Enhanced Access Control

Abstract

As the artificial intelligence, large model, Metaverse, and Web 3.0 develop rapidly, data is being traded constantly. Existing data exchange methods primarily rely on trusted third parties, which compromises fairness and decentralization. Moreover, existing methods often overlook data access control during trading and typically employ an one-to-one model, resulting in high communication and computational overhead. To address these issues, this article makes the following contributions. First, we propose a blockchain-based secure and fair data trading scheme named fair data trading (FairDT). By leveraging blockchain and smart contracts, FairDT achieves decentralized data trading with high throughput and scalability. Second, we design a fair data exchange mechanism that utilizes commitment schemes, Merkle trees, and other techniques to facilitate dispute resolution with constant on-chain cost when conflicts arise. Third, we incorporate attribute-based encryption to enable fine-grained access control in data trading, thereby reducing the computational burden on data sellers. Finally, we prove that FairDT satisfies access control, fair exchange, completeness, and termination properties. Experimental results on the Ethereum testnet demonstrate that the on-chain cost remains constant, showing that FairDT is highly efficient.