Masquerade: Simple and Lightweight Transaction Reordering Mitigation in Blockchains
- URL: http://arxiv.org/abs/2308.15347v1
- Date: Tue, 29 Aug 2023 14:42:43 GMT
- Title: Masquerade: Simple and Lightweight Transaction Reordering Mitigation in Blockchains
- Authors: Arti Vedula, Shaileshh Bojja Venkatakrishnan, Abhishek Gupta,
- Abstract summary: We propose an MEV aware protocol design called Masquerade to increase user satisfaction and confidence in the system.
We introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario.
- Score: 5.690884793952696
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Blockchains offer strong security gurarantees, but cannot protect users against the ordering of transactions. Players such as miners, bots and validators can reorder various transactions and reap significant profits, called the Maximal Extractable Value (MEV). In this paper, we propose an MEV aware protocol design called Masquerade, and show that it will increase user satisfaction and confidence in the system. We propose a strict per-transaction level of ordering to ensure that a transaction is committed either way even if it is revealed. In this protocol, we introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario. Such tokens can be purchased voluntarily by users, who can then choose to include the token numbers in their transactions. If the users include the token in their transactions, then our protocol requires the block-builder to order the transactions strictly according to token numbers. We show through extensive simulations that this reduces the probability that the adversaries can benefit from MEV transactions as compared to existing current practices.
Related papers
- BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
BlockFound is a customized foundation model for anomaly blockchain transaction detection.
We introduce a series of customized designs to model the unique data structure of blockchain transactions.
BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
arXiv Detail & Related papers (2024-10-05T05:11:34Z) - The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols.
Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol.
We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
arXiv Detail & Related papers (2024-07-16T20:53:04Z) - Transaction Fee Estimation in the Bitcoin System [11.065598886291735]
In the Bitcoin system, transaction fees serve as an incentive for blockchain confirmations.
In this work, we focus on estimating the transaction fee for a new transaction to help with its confirmation within a given expected time.
We propose a framework FENN, which aims to integrate the knowledge from a wide range of sources, including the transaction itself, into a neural network model in order to estimate a proper transaction fee.
arXiv Detail & Related papers (2024-05-24T07:27:00Z) - Sequencer Level Security [2.756899615600916]
We introduce the Sequencer Level Security (SLS) protocol, an enhancement to sequencing protocols of rollups.
We describe the mechanics of the protocol for both the transactions submitted to the rollup mempool, as well as transactions originating from Layer one.
We implement a prototype of the SLS protocol, Zircuit, which is built on top of Geth and the OP stack.
arXiv Detail & Related papers (2024-05-03T02:47:40Z) - Towards Stronger Blockchains: Security Against Front-Running Attacks [10.220888127527152]
We show that total ordering is not strong enough to preserve application semantics under Byzantine fault model.
This is due to the fact that malicious miners and clients can collaborate to add their own transactions ahead of correct clients' transactions in order to gain application level and financial advantages.
We propose preserving causality preserving total order as a solution.
arXiv Detail & Related papers (2023-11-17T00:50:49Z) - Blockchain Large Language Models [65.7726590159576]
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions.
The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
arXiv Detail & Related papers (2023-04-25T11:56:18Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems [37.22526235663589]
This paper focuses on the transaction allocation problem to reduce the number of cross-shard transactions.
A deterministic and fast allocation scheme TxAllo is proposed to dynamically infer the allocation of accounts.
For a blockchain with 60 shards, TxAllo reduces the cross-shard transaction ratio from 98% to about 12%.
arXiv Detail & Related papers (2022-12-22T10:22:31Z) - Light Clients for Lazy Blockchains [12.330989180881701]
We devise a protocol that enables the creation of efficient light clients for lazy blockchains.
Our construction is based on a bisection game that traverses the Merkle tree containing the ledger of all - valid or invalid - transactions.
arXiv Detail & Related papers (2022-03-30T00:58:40Z) - CubeFlow: Money Laundering Detection with Coupled Tensors [39.26866956921283]
Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities.
Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts.
CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions.
arXiv Detail & Related papers (2021-03-23T09:24:31Z) - An Efficient Permissioned Blockchain with Provable Reputation Mechanism [2.579878570919875]
We study a hierarchical scenario to include three types of participants: providers, collectors, and governors.
We introduce a reputation protocol as a measure of the reliability of collectors in the permissioned blockchain environment.
arXiv Detail & Related papers (2020-02-17T09:25:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.