Paper 2023/1061

BlindPerm: Efficient MEV Mitigation with an Encrypted Mempool and Permutation

Alireza Kavousi, University College London
Duc V. Le, Visa Research
Philipp Jovanovic, University College London
George Danezis, Mysten Labs, University College London
Abstract

To mitigate the negative effects of the maximal extractable value (MEV), we propose techniques that utilize randomized permutation to shuffle the order of transactions in a committed block before execution. We argue that existing approaches based on encrypted mempools cannot provide sufficient mitigation, particularly against block producer, and can be extended by permutation-based techniques to provide multi-layer protection. With a focus on PoS committee-based consensus we then introduce BlindPerm, a framework enhancing an encrypted mempool with permutation and present various optimizations. Notably, we propose a protocol where this enhancement comes at essentially no overheads by piggybacking on the encrypted mempool. Further, we demonstrate how to extend our mitigation technique to support PoW longest-chain consensus. Finally, we illustrate the effectiveness of our solutions on arbitrage and sandwich attacks as the two main types of MEV extraction through running simulations using historical Ethereum data.

Metadata
Available format(s)
PDF
Category
Cryptographic protocols
Publication info
Preprint.
Keywords
maximal extractable valueMEVorder fairnessencrypted mempool
Contact author(s)
a kavousi @ cs ucl ac uk
duc le @ visa com
p jovanovic @ ucl ac uk
george @ mystenlabs com
History
2025-01-15: last of 2 revisions
2023-07-07: received
See all versions
Short URL
https://ia.cr/2023/1061
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1061,
      author = {Alireza Kavousi and Duc V. Le and Philipp Jovanovic and George Danezis},
      title = {{BlindPerm}: Efficient {MEV} Mitigation with an Encrypted Mempool and Permutation},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1061},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1061}
}
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