A Two-Way Atomic Exchange Protocol for Peer-to-Peer Data Trading

Zan-Jun Wang,Ching-Chun Huang,Shih-Wei Liao, Zih-shiuan Spin Yuan


引用 0|浏览2
Various types of data are generated every day, and the amount of generated data is growing exponentially. People are interested in extracting the value of data. Some valuable data among them can be viewed as digital products to be traded. For example, with the outbreak of COVID-19, patients' personal health records, electronic medical records and travel history become important and valuable information for epidemic prevention. On the other hand, the license keys for high-priced software such as EDA (Electronic Design Automation) tools also have value and can be considered as tradable products. However, the trust between two parties in the trading process becomes an issue. Consumers do not pay until providers give the data while providers are not willing to do since they distrust that consumers will pay after receiving the data. In this paper, we propose a Blockchain-based protocol for data trading with zero-knowledge proofs. To protect the data and maintain their value, Zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) is included that the provider can convince the consumer of the correctness and security of the data without revealing the details before receiving the payment. Predefined agreements between both parties in smart contracts are executed automatically. When the data is valid, the provider receives the payment, and in the meantime, the consumer has the ability to obtain the purchase data. Otherwise, the payment is refunded to the consumer immediately if the provider cheats. This approach employs the method of two-way exchange, as known as Delivery versus Payment (DvP) in physical commodity trading and ensures the rights and benefits for both parties. The whole process is decentralized for the purpose of constructing fair data trading without any trusted third party and ensuring system availability.
Blockchain, Zero knowledge, Decentralization, Data trading, Confidentiality, Data privacy
AI 理解论文
Chat Paper