Towards Robust Blockchain Price Oracle: A Study on Human-Centric Node Selection Strategy and Incentive Mechanism

TL;DR

This paper introduces a secure, incentive-compatible node selection and payment mechanism for blockchain oracles, enhancing data reliability and participant engagement in financial applications.

Contribution

It proposes an anonymous high-reputation node selection scheme and a Stackelberg game-based incentive mechanism to improve security and participation in blockchain oracles.

Findings

Reduces price data variance by about 55%

Ensures security through security analysis

Balances revenue among participants

Abstract

As a trusted middleware connecting the blockchain and the real world, the blockchain oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. However, the current oracle schemes face the dilemma of security and service quality in the process of node selection, and the implicit interest relationship in financial applications leads to a significant conflict of interest between the task publisher and the executor, which reduces the participation enthusiasm of both parties and system security. Therefore, this paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks to ensure the security and service quality of nodes. Then, this paper also details the interest requirements and behavioral motives of all parties in the payment settlement and asset valuation scenarios. Under the hypothesis of rational man, an incentive mechanism based on the Stackelberg game is proposed. It can achieve equilibrium under the pursuit of the revenue of task publishers and executors, thereby ensuring the revenue of all types of users and improving the enthusiasm for participation. Finally, we verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can reduce the variance of obtaining price data by about 55\% while ensuring security, and meeting the revenue of all parties.