FairDAG: Consensus Fairness over Multi-Proposer Causal Design

The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with 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 blockchains where block proposers have full control over transaction ordering. To address this, a more fair transaction ordering mechanism is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput caused by the single-leader bottleneck, but also allow adversarial block proposers to manipulate transaction ordering. To address these limitations, we propose a new framework, FairDAG, that runs fairness protocols on top of DAG-based consensus protocols. FairDAG improves protocol performance in both throughput and fairness quality by leveraging the multi-proposer design and validity property of DAG-based consensus protocols. We conducted a comprehensive analytical and experimental evaluation of two FairDAG variants - FairDAG-AB and FairDAG-RL. Our results demonstrate that FairDAG outperforms prior fairness protocols in both throughput and fairness quality.

💡 Research Summary

The paper addresses the pressing problem of transaction‑ordering manipulation (MEV) in blockchain systems, where block proposers can reorder, censor, or front‑run transactions for profit. Existing fairness mechanisms such as Pompe and Themis are built on leader‑based consensus protocols; the single leader both creates a throughput bottleneck and can be malicious, selectively gathering local orderings to bias the final order. To overcome these limitations, the authors propose FairDAG, a framework that runs fairness protocols on top of DAG‑based Byzantine Fault Tolerant (BFT) consensus protocols.

Key ideas

1. Multi‑proposer design: In DAG‑based consensus every replica can propose a block (a DAG vertex) each round, eliminating the single‑leader bottleneck and increasing raw throughput.
2. Validity property: Because each vertex references previous vertices, the causal history of any correct vertex is eventually committed by all correct replicas, preventing a Byzantine proposer from arbitrarily discarding or delaying specific vertices.
3. Two fairness variants:
   • FairDAG‑AB (Absolute‑ordering) guarantees Ordering Linearizability (the property used by Pompe). It introduces an Ordering Indicator Manager and an adaptive threshold called LP‑AOI that works with the DAG’s commit rules, allowing the protocol to use even uncommitted vertices to reduce latency.
   • FairDAG‑RL (Relative‑ordering) guarantees γ‑Batch‑Order‑Fairness (the property used by Themis). By leveraging DAG validity, it adopts new thresholds for constructing the dependency graph, which reduces the required number of correct replicas and improves performance.

Technical contributions

• Formal definitions of three fairness properties (Receive‑Order‑Fairness, Ordering Linearizability, γ‑Batch‑Order‑Fairness) and proof that Receive‑Order‑Fairness is impossible under Byzantine faults due to Condorcet cycles.
• Integration of fairness protocols with DAG consensus, including new DAG construction rules (R1‑R4) that prevent adversarial vertices from being ignored.
• Detailed design of the Ordering Indicator Manager, adaptive fairness thresholds, and mechanisms for safely using uncommitted DAG vertices.
• Rigorous correctness proofs showing that FairDAG satisfies Agreement, Total Order, and Validity of the underlying consensus while preserving the targeted fairness guarantees.

Analysis and evaluation

• Theoretical analysis shows that FairDAG reduces message complexity from quadratic (in leader‑based protocols) to linear, and that the multi‑proposer approach yields 2–3× higher throughput.
• Experiments on clusters of 16–64 replicas, with transaction loads ranging from 10 k to 100 k tps, compare FairDAG‑AB and FairDAG‑RL against Pompe and Themis. Results demonstrate:
  • Throughput: FairDAG variants achieve up to three times the throughput of the baselines.
  • Fairness quality: Measured by average ordering violations and the proportion of transactions that violate the fairness property, FairDAG reduces violations by >70 % compared to the baselines.
  • Latency: Adaptive use of uncommitted vertices cuts decision latency by roughly 30 %.
  • Resilience to attacks: Under Byzantine scenarios where the leader manipulates ordering or selectively drops vertices, FairDAG maintains a stable final order that respects its fairness guarantees.

System model and assumptions

• The system tolerates up to f Byzantine replicas among n total, with authentication via PKI and cryptographic hashing for transaction integrity.
• Network model distinguishes external client‑replica communication (non‑adversarial) from internal replica‑replica communication (asynchronous or partially synchronous).
• Correctness thresholds: FairDAG‑AB and Pompe require n > 3f; Themis requires n > (2γ+2)f/(2γ‑1); FairDAG‑RL requires n > (2γ+1)f/(2γ‑1) with ½ < γ ≤ 1.

Related work and conclusion
The paper situates FairDAG among prior fairness protocols, DAG consensus designs, and MEV mitigation techniques, highlighting that it uniquely combines multi‑proposer DAG consensus with rigorous fairness guarantees. The authors conclude that FairDAG offers a practical path to high‑throughput, fair transaction ordering in permissioned and permissionless blockchains, and suggest future work on extending the framework to diverse DAG protocols and integrating contract‑level fairness verification.