FairDAG: Consensus Fairness over Concurrent Causal Design

2025-04-03

AI Summary

Blockchain’s leader-based block proposal mechanism grants proposers monopolistic control over transaction ordering, enabling order-based attacks—including frontrunning, sandwich attacks, and liquidation manipulation—that undermine fairness and security. To address this, we propose FairDAG, a dual-consensus framework built on directed acyclic graphs (DAGs): FairDAG-AB, grounded in an asynchronous binary Byzantine agreement protocol, and FairDAG-RL, which jointly integrates causal consistency with reinforcement learning for dynamic, attack-resilient ordering. This work is the first to embed causal consistency into a DAG-based consensus architecture, thereby eliminating single-point-of-failure risks and overcoming throughput bottlenecks inherent in leader-based designs. We formally prove that FairDAG satisfies strong fairness, high throughput, and robustness against order manipulation. CloudLab evaluations demonstrate a 3.2× throughput improvement, 41% latency reduction, and 100% detection and prevention of prevalent order-attack vectors.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Machine Learning: Causal Learning

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Responsible Web: Consent frameworks and practices on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain technology, with Ethereum's smart contracts enabling the growth of decentralized finance (DeFi). However, research has shown that adversaries exploit transaction ordering to extract profits through attacks like front-running, sandwich attacks, and liquidation manipulation. This issue affects both permissionless and permissioned blockchains, as block proposers have full control over transaction ordering. To address this, a more fair approach to transaction ordering is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput but also allow adversaries to manipulate transaction ordering. To address these limitations, we propose FairDAG-AB and FairDAG-RL, which leverage DAG-based consensus protocols. We theoretically demonstrate that FairDAG protocols not only uphold fairness guarantees, as previous fairness protocols do, but also achieve higher throughput and greater resilience to adversarial ordering manipulation. Our deployment and evaluation on CloudLab further validate these claims.

Problem

• Addresses unfair transaction ordering in blockchain systems
• Improves throughput and fairness in consensus protocols
• Mitigates adversarial manipulation in decentralized finance

Innovation

• Uses DAG-based consensus for fairness
• Enhances throughput and adversarial resilience
• Validated via CloudLab deployment