An Incentive-Compatible Reward Sharing Mechanism for Mitigating Mirroring Attacks in Decentralized Data-Feed Systems

2025-09-14

AI Summary

Mirror attacks—where adversaries deploy multiple Sybil oracles to manipulate aggregated outputs and arbitrage—arise from flawed incentive mechanisms in decentralized oracle networks. Method: We propose an incentive-compatible reward allocation scheme, formally modeled via game theory. For the first time, we rigorously prove within a majority-voting framework that a carefully designed reward function induces a Nash equilibrium wherein rational users voluntarily operate exactly one oracle, thereby eliminating the economic incentive for Sybil-style manipulation. Contribution/Results: Our theoretical analysis establishes existence and uniqueness of this equilibrium; numerical simulations confirm substantial reductions in attack incentives, alongside improved aggregation accuracy and robustness against manipulation. Crucially, we tightly couple incentive design with consensus reliability, yielding the first provably secure, lightweight incentive mechanism for decentralized oracle systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• Security and Privacy: Large-scale security measurements

Abstract

Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.

Problem

• Mitigating mirroring attacks in decentralized data-feed systems
• Analyzing impact of Sybil attacks on majority voting reliability
• Proposing incentive-compatible mechanism to discourage multi-oracle control

Innovation

• Incentive-compatible reward sharing mechanism
• Discourages Sybil behavior via Nash Equilibrium
• Mitigates mirroring attacks in decentralized systems