发表机构
Beihang University; Jinan University; Nanyang Technological University（北京航空航天大学; 暨南大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FlashOrder是一种确定性公平排序引擎，通过局部化循环歧义替代全批次SCC凝聚，在基于libhotstuff的原型上，其吞吐量较Themis、Rashnu显著提升，同时降低最大秩位移，实现更强公平性与更高可扩展性。
AI 中文摘要
在区块链系统中，交易排序直接决定财务结果：不公平排序会导致抢先交易和三明治攻击，已从以太坊用户处提取超过6.86亿美元。当前的公平排序协议会聚合来自副本的成对接收顺序证据。然而，在竞争或对抗性操纵下，康多塞（Condorcet）循环会迫使它们进入全局强连通分量（SCC）凝聚，导致延迟、粗批次和扩展失败。我们提出FlashOrder，一种确定性公平排序引擎，可在循环歧义传播到整个批次前将其局部化。FlashOrder将成对偏好嵌入一维规范位置，用划分超图聚类邻近交易，并执行分层的簇间和簇内序列化，用局部排序和聚合替代全批次SCC凝聚。在基于libhotstuff的原型上，针对Themis（CCS '23）和Rashnu（VLDB '24）进行评估，FlashOrder的吞吐量比Themis高10.5倍，比Rashnu高4.8倍，且延迟差距随网络规模扩大而增大。在受控对抗模拟中，它将最大秩位移降低88.7%，在康多塞攻击下，其平均吞吐量比Themis高12.0倍，比Rashnu高9.7倍。这些结果表明，局部化循环歧义可在显著提高吞吐量的同时实现更强的公平性。
英文摘要
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.
