Breaking Cycles for Scalable Fair Ordering in Blockchain Systems

2026-09-01

AI Summary

为解决区块链系统中不公平交易排序问题，FlashOrder通过局部化循环模糊并使用分层序列化方法提高交易处理的公平性和吞吐量。

Abstract

In blockchain systems, transaction order directly determines financial outcomes: unfair ordering enables front-running and sandwich attacks that have extracted over \$686M from Ethereum users. Current fair-ordering protocols aggregate pairwise receive-order evidence from replicas. Under contention or adversarial manipulation, however, Condorcet cycles force them into global strongly connected component (SCC) condensation, causing delays, coarse batches, and scaling failures. We present FlashOrder, a deterministic fair-ordering engine that localizes cyclic ambiguity before it propagates across the batch. FlashOrder embeds pairwise preferences into one-dimensional canonical positions, clusters nearby transactions with a partition hypergraph, and performs hierarchical inter- and intra-cluster serialization, replacing batch-wide SCC condensation with localized sorting and aggregation. Evaluated against Themis (CCS '23) and Rashnu (VLDB '24) on a libhotstuff-based prototype, FlashOrder achieves up to 10.5$\times$ higher throughput than Themis and 4.8$\times$ higher than Rashnu, with the latency gap widening as network scales. In controlled adversarial simulation, it reduces maximum rank displacement by 88.7\%, and under Condorcet attacks it sustains 12.0$\times$ and 9.7$\times$ higher throughput than Themis and Rashnu on average. These results show that localizing cyclic ambiguity yields stronger fairness at substantially higher throughput.

Problem

• transaction order
• fair ordering
• blockchain systems
• Condorcet cycles
• SCC condensation

Innovation

• deterministic fair-ordering
• localizing cyclic ambiguity
• partition hypergraph
• hierarchical serialization
• SCC condensation