PROF：逐利世界中的受保护订单流
PROF: Protected Order Flow in a Profit-Seeking World
AI总结：
针对 PBS 架构下交易排序操纵导致的有害 MEV，本文提出 PROF 系统，通过强制私有交易包顺序并激励区块生产者及时纳入，实现兼容、低延迟且无需额外信任的保护。
摘要：
去中心化金融（DeFi）应用的用户面临来自对抗性行为的重大风险，这些行为通过操纵交易顺序从用户手中榨取价值。此类行为——一种被称为最大可提取价值（MEV）的对抗性形式——既影响个体结果，也影响 DeFi 生态系统的稳定性。此外，MEV 剥削正通过一种被称为 Proposer-Builder Separation（PBS，提议者-构建者分离）的架构范式走向制度化。本工作提出了一个名为 PROF（PRotected Order Flow，受保护订单流）的系统，旨在限制现有 PBS 系统中有害形式的 MEV。PROF 通过两个思路实现这一目标。首先，PROF 对一组（“bundle”，交易包）私下输入的交易施加顺序，并将该顺序一直强制执行至区块生产环节——从而防止交易顺序操纵。其次，PROF 创建的交易包对区块生产者而言包含其中是有利可图的，从而确保交易包能够及时被纳入区块。PROF 具备向后兼容性，这意味着它可与现有及未来的 PBS 设计协同工作。PROF 还兼容在 PROF 交易包内排序交易的任何所需算法（例如先到先服务、基于费用等）。它执行高效，即具有低延迟，并且不需要 PBS 各实体之间作出额外的信任假设。我们定量和定性地分析了 PROF 的激励结构，以及与现有解决方案相比它对用户的效用。我们还报告了 PROF 交易的纳入可能性，以及通过我们的端到端实现得到的具体延迟数据。
英文摘要：
Users of decentralized finance (DeFi) applications face significant risks from adversarial actions that manipulate the order of transactions to extract value from users. Such actions -- an adversarial form of what is called maximal-extractable value (MEV) -- impact both individual outcomes and the stability of the DeFi ecosystem. MEV exploitation, moreover, is being institutionalized through an architectural paradigm known Proposer-Builder Separation (PBS). This work introduces a system called PROF (PRotected Order Flow) that is designed to limit harmful forms of MEV in existing PBS systems. PROF aims at this goal using two ideas. First, PROF imposes an ordering on a set ("bundle") of privately input transactions and enforces that ordering all the way through to block production -- preventing transaction-order manipulation. Second, PROF creates bundles whose inclusion is profitable to block producers, thereby ensuring that bundles see timely inclusion in blocks. PROF is backward-compatible, meaning that it works with existing and future PBS designs. PROF is also compatible with any desired algorithm for ordering transactions within a PROF bundle (e.g., first-come, first-serve, fee-based, etc.). It executes efficiently, i.e., with low latency, and requires no additional trust assumptions among PBS entities. We quantitatively and qualitatively analyze incentive structure of PROF, and its utility to users compared with existing solutions. We also report on inclusion likelihood of PROF transactions, and concrete latency numbers through our end-to-end implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
