Multiple Proposer Transaction Fee Mechanism Design: Robust Incentives Against Censorship and Bribery
- URL: http://arxiv.org/abs/2505.13751v1
- Date: Mon, 19 May 2025 21:53:58 GMT
- Title: Multiple Proposer Transaction Fee Mechanism Design: Robust Incentives Against Censorship and Bribery
- Authors: Aikaterini-Panagiota Stouka, Julian Ma, Thomas Thiery,
- Abstract summary: This study explores how multiple proposers should be rewarded to incentivize censorship resistance.<n>Main contribution is the identification of TFMs that ensure censorship resistance under bribery attacks.<n>We provide a concrete payment mechanism for FOCIL, along with generalizable contributions to the literature.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Censorship resistance is one of the core value proposition of blockchains. A recurring design pattern aimed at providing censorship resistance is enabling multiple proposers to contribute inputs into block construction. Notably, Fork-Choice Enforced Inclusion Lists (FOCIL) is proposed to be included in Ethereum. However, the current proposal relies on altruistic behavior, without a Transaction Fee Mechanism (TFM). This study aims to address this gap by exploring how multiple proposers should be rewarded to incentivize censorship resistance. The main contribution of this work is the identification of TFMs that ensure censorship resistance under bribery attacks, while also satisfying the incentive compatibility properties of EIP-1559. We provide a concrete payment mechanism for FOCIL, along with generalizable contributions to the literature by analyzing 1) incentive compatibility of TFMs in the presence of a bribing adversary, 2) TFMs in protocols with multiple phases of transaction inclusion, and 3) TFMs of protocols in which parties are uncertain about the behavior and the possible bribe of others.
Related papers
- Information Bargaining: Bilateral Commitment in Bayesian Persuasion [60.3761154043329]
We introduce a unified framework and a well-structured solution concept for long-term persuasion.<n>This perspective makes explicit the common knowledge of the game structure and grants the receiver comparable commitment capabilities.<n>The framework is validated through a two-stage validation-and-inference paradigm.
arXiv Detail & Related papers (2025-06-06T08:42:34Z) - Adversary-Augmented Simulation for Fairness Evaluation and Defense in Hyperledger Fabric [0.0]
This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple protocols.<n>Our model classifies and constrains adversarial actions based on the assumptions of the target protocols.<n>We apply this framework to analyze fairness properties in a Hyperledger Fabric (HF) blockchain network.
arXiv Detail & Related papers (2025-04-17T08:17:27Z) - Hollow Victory: How Malicious Proposers Exploit Validator Incentives in Optimistic Rollup Dispute Games [2.88268082568407]
A popular layer-2 approach is the Optimistic Rollup, which relies on a mechanism known as a dispute game for block proposals.<n>In these systems, validators can challenge blocks that they believe contain errors, and a successful challenge results in the transfer of a portion of the proposer's deposit as a reward.<n>We reveal a structural vulnerability in the mechanism: validators may not be awarded a proper profit despite winning a dispute challenge.
arXiv Detail & Related papers (2025-04-07T14:00:46Z) - To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models [56.19026073319406]
Large Reasoning Models (LRMs) are designed to solve complex tasks by generating explicit reasoning traces before producing final answers.<n>We reveal a critical vulnerability in LRMs -- termed Unthinking -- wherein the thinking process can be bypassed by manipulating special tokens.<n>In this paper, we investigate this vulnerability from both malicious and beneficial perspectives.
arXiv Detail & Related papers (2025-02-16T10:45:56Z) - It Takes Two: A Peer-Prediction Solution for Blockchain Verifier's Dilemma [12.663727952216476]
We develop a Byzantine-robust peer prediction framework towards the design of one-phase Bayesian truthful mechanisms for the decentralized verification games.<n>Our study provides a framework of incentive design for decentralized verification protocols that enhances the security and robustness of the blockchain.
arXiv Detail & Related papers (2024-06-03T21:21:17Z) - Designing Redistribution Mechanisms for Reducing Transaction Fees in
Blockchains [10.647087323578477]
Transaction Fee Mechanisms (TFMs) determine which user transactions to include in blocks and determine their payments.
We propose Transaction Fee Redistribution Mechanisms (TFRMs) -- redistributing VCG payments as rebates to minimize transaction fees.
Our results show that TFRMs provide a promising new direction for reducing transaction fees in public blockchains.
arXiv Detail & Related papers (2024-01-24T07:09:32Z) - Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? [52.238883592674696]
Ring-A-Bell is a model-agnostic red-teaming tool for T2I diffusion models.
It identifies problematic prompts for diffusion models with the corresponding generation of inappropriate content.
Our results show that Ring-A-Bell, by manipulating safe prompting benchmarks, can transform prompts that were originally regarded as safe to evade existing safety mechanisms.
arXiv Detail & Related papers (2023-10-16T02:11:20Z) - Towards a Theory of Maximal Extractable Value II: Uncertainty [4.07926531936425]
Maximal Extractable Value (MEV) is value extractable by temporary monopoly power commonly found in decentralized systems.
This extraction stems from a lack of user privacy upon transaction submission and the ability of a monopolist validator to reorder, add, and/or censor transactions.
We show that neither fair ordering techniques nor economic mechanisms can individually mitigate MEV for arbitrary payoff functions.
arXiv Detail & Related papers (2023-09-25T15:01:11Z) - Cooperation or Competition: Avoiding Player Domination for Multi-Target
Robustness via Adaptive Budgets [76.20705291443208]
We view adversarial attacks as a bargaining game in which different players negotiate to reach an agreement on a joint direction of parameter updating.
We design a novel framework that adjusts the budgets of different adversaries to avoid any player dominance.
Experiments on standard benchmarks show that employing the proposed framework to the existing approaches significantly advances multi-target robustness.
arXiv Detail & Related papers (2023-06-27T14:02:10Z) - 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) - Semantic Information Marketing in The Metaverse: A Learning-Based
Contract Theory Framework [68.8725783112254]
We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data.
Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices.
We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
arXiv Detail & Related papers (2023-02-22T15:52:37Z) - VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit
Feedback [104.06766271716774]
We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values.
We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism.
Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
arXiv Detail & Related papers (2020-04-19T18:00:58Z)
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.