Safeguarding the Truth of High-Value Price Oracle Task: A Dynamically Adjusted Truth Discovery Method

TL;DR

The paper tackles the fragility of price oracles in DeFi due to high-value attacks. It introduces a dynamically adjusted truth discovery (DATD) framework that combines a two-stage truth discovery with a temporal-difference–inspired future credibility estimate and a task-value/CPEC–based credibility update, plus a commit-reveal mechanism to prevent freeloading. Empirical results show that DATD reduces data deviation by 65.8% and potential economic loss by 66.5% compared with a baseline under high-value attacks, using a network of 20 data sources and 20 oracle nodes. The work improves oracle reliability for DeFi smart contracts by making truth estimation more resilient to attacks and by recognizing that high-value tasks require stronger, memory-informed credibility assessment.

Abstract

In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamically adjusted truth discovery method safeguarding the truth of high-value price oracle tasks. In the truth aggregation stage, we enhance future considerations to improve the precision of aggregated truth. During the credibility update phase, credibility is dynamically assessed based on the task's value and the Cumulative Potential Economic Contribution (CPEC) of information sources. Experimental results demonstrate a significant reduction in data deviation by 65.8\% and potential economic loss by 66.5\%, compared to the baseline scheme, in the presence of high-value attacks.

Figures (7)

• Figure 1: Overview of Oracle system.
• Figure 2: The process of two-stage TD in the proposed scheme.
• Figure 3: Data deviation and economic loss under different conditions. (a)-(b) is the data deviation under high-value attacks of 10% and 30%; (c) is a possible economic loss under high-value attacks of 10%.
• Figure 4: The aggregation weight for each truth aggregation (Second TD).
• Figure 5: Changes in CPEC of nodes.