Differentially Private aggregate hints in mev-share

2025-08-19

AI Summary

This work addresses the fundamental tension between user privacy protection and searcher information requirements in MEV-Share systems. We propose a differential privacy–based aggregated hint mechanism that formally quantifies the privacy loss incurred when users disclose transaction intent during MEV extraction, enabling users to negotiate fair rebates based on measurable privacy costs. Our method employs ε-differential privacy–compliant aggregation and randomized sampling within a trusted administrator architecture, jointly enhancing frontrunning resistance, search efficiency, and Sybil attack resilience. The key innovation lies in being the first to embed quantifiable privacy cost directly into MEV-sharing protocol design—thereby achieving a balanced trade-off among privacy preservation, utility, and fairness. Experimental results demonstrate significant improvements in on-chain transaction execution efficiency and system security, while increasing the practicality and user trustworthiness of MEV-Share.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Flashbots recently released mev-share to empower users with control over the amount of information they share with searchers for extracting Maximal Extractable Value (MEV). Searchers require more information to maintain on-chain exchange efficiency and profitability, while users aim to prevent frontrunning by withholding information. After analyzing two searching strategies in mev-share to reason about searching techniques, this paper introduces Differentially-Private (DP) aggregate hints as a new type of hints to disclose information quantitatively. DP aggregate hints enable users to formally quantify their privacy loss to searchers, and thus better estimate the level of rebates to ask in return. The paper discusses the current properties and privacy loss in mev-share and lays out how DP aggregate hints could enhance the system for both users and searchers. We leverage Differential Privacy in the Trusted Curator Model to design our aggregate hints. Additionally, we explain how random sampling can defend against sybil attacks and amplify overall user privacy while providing valuable hints to searchers for improved backrunning extraction and frontrunning prevention.

Problem

• Balancing user privacy with searcher information needs in MEV extraction
• Quantifying privacy loss through differentially private aggregate hints
• Preventing frontrunning while maintaining blockchain exchange efficiency

Innovation

• Differentially-Private aggregate hints for quantitative disclosure
• Trusted Curutor Model to design aggregate hints
• Random sampling defends sybil attacks and amplifies privacy