PROF: Protected Order Flow in a Profit-Seeking World

• URL: http://arxiv.org/abs/2408.02303v1
• Date: Mon, 5 Aug 2024 08:26:10 GMT
• Title: PROF: Protected Order Flow in a Profit-Seeking World
• Abstract summary: This work introduces a system called PROF that is designed to limit harmful forms of maximal-extractable value (MEV) in existing PBS systems. PROF imposes an ordering on a set ("bundle") of privately input transactions and enforces that ordering all the way through to block production. We quantitatively and qualitatively analyze incentive structure of PROF, and its utility to users compared with existing solutions.
• Score: 8.76739307907209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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