Differentially Private aggregate hints in mev-share

TL;DR

This paper introduces Differentially Private aggregate hints in mev-share, enabling users to quantify privacy loss and balance information sharing with searchers to improve MEV extraction and prevent frontrunning.

Contribution

It proposes a novel DP aggregate hints mechanism in mev-share, leveraging differential privacy and sampling to enhance privacy and system efficiency.

Findings

DP aggregate hints quantify privacy loss effectively.

Sampling defends against sybil attacks and amplifies privacy.

Enhanced hints improve backrunning extraction and frontrunning prevention.

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.