CoBRA: A Universal Strategyproof Confirmation Protocol for Quorum-based Proof-of-Stake Blockchains

• URL: http://arxiv.org/abs/2503.16783v1
• Date: Fri, 21 Mar 2025 01:39:29 GMT
• Title: CoBRA: A Universal Strategyproof Confirmation Protocol for Quorum-based Proof-of-Stake Blockchains
• Authors: Zeta Avarikioti, Eleftherios Kokoris Kogias, Ray Neiheiser, Christos Stefo,
• Abstract summary: We present a formal analysis of quorum-based State Machine Replication (SMR) protocols in Proof-of-Stake (PoS) systems under a hybrid threat model comprising honest, Byzantine, and rational validators.<n>Our analysis of traditional quorum-based protocols establishes two fundamental impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators comprise more than $1/3$ of participants, and (2) in synchronous networks, SMR remains impossible when rational and Byzantine validators comprise $2/3$ or more of participants.
• Score: 1.5761916307614148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a formal analysis of quorum-based State Machine Replication (SMR) protocols in Proof-of-Stake (PoS) systems under a hybrid threat model comprising honest, Byzantine, and rational validators. Our analysis of traditional quorum-based protocols establishes two fundamental impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators comprise more than $1/3$ of participants, and (2) in synchronous networks, SMR remains impossible when rational and Byzantine validators comprise $2/3$ or more of participants. To overcome these limitations, we propose two complementary solutions in our hybrid model. First, we introduce a protocol that enforces a bound on the volume of the total transacted amount that is finalized within any time window $\Delta$ and prove that this bound is necessary for secure SMR protocols in our model. Second, we present the \emph{strongest chain rule}, which enables efficient finalization of transactions when the majority of honest participants provably support the SMR execution. Through empirical analysis of Ethereum and Cosmos networks, we demonstrate that validator participation consistently exceeds the required ${5}/{6}$ threshold, establishing the practical feasibility of our solution in production PoS systems.

Related papers

• 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. 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. 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)
• Commit-Reveal$^2$: Randomized Reveal Order Mitigates Last-Revealer Attacks in Commit-Reveal [0.0]
  Commit-Reveal$2$ protocol employs a two-layer Commit-Reveal process to randomize the reveal order and mitigate the risk of such attacks. We implement a prototype of the proposed mechanism and publicly release the code to facilitate practical adoption and further research.
  arXiv  Detail & Related papers  (2025-04-04T21:05:51Z)
• Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness [61.87055159919641]
  Multi-modal semantic segmentation (MMSS) addresses the limitations of single-modality data by integrating complementary information across modalities. Despite notable progress, a significant gap persists between research and real-world deployment due to variability and uncertainty in multi-modal data quality. We introduce a robustness benchmark that evaluates MMSS models under three scenarios: Entire-Missing Modality (EMM), Random-Missing Modality (RMM), and Noisy Modality (NM)
  arXiv  Detail & Related papers  (2025-03-24T08:46:52Z)
• Fractional Spending: VRF&Ring Signatures As Efficient Primitives For Secret Quorums [0.0]
  Digital currencies face challenges in distributed settings, particularly regarding double spending.<n>Traditional approaches, such as Bitcoin, use consensus to establish a total order of transactions.<n>This paper enhances such solution by integrating different cryptographic primitives, VRF and Ring Signatures, into a similar protocol.
  arXiv  Detail & Related papers  (2024-12-21T14:37:36Z)
• Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users [95.77678166036561]
  We propose a UCB-type algorithm with delicate communication protocols. We give sub-linear regret bounds on par with those achieved in the synchronous framework. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.
  arXiv  Detail & Related papers  (2024-02-26T05:31:14Z)
• Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks [46.689212344009015]
  This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for data management and resource allocation in mobile edge computing networks. We design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs. We provide extensive simulation results and analysis to validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and MEC resource allocation algorithms.
  arXiv  Detail & Related papers  (2023-12-13T09:39:32Z)
• Trustless Privacy-Preserving Data Aggregation on Ethereum with Hypercube Network Topology [0.0]
  We have proposed a scalable privacy-preserving data aggregation protocol for summation on the blockchain. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification.
  arXiv  Detail & Related papers  (2023-08-29T12:51:26Z)
• Simple Opinion Dynamics for No-Regret Learning [38.61048016579232]
  We study a cooperative multi-agent bandit setting in the distributed GOSSIP model. We introduce and analyze families of memoryless and time-independent protocols for this setting. For stationary reward settings, we prove for the first time that these simple protocols exhibit best-of-both-worlds behavior.
  arXiv  Detail & Related papers  (2023-06-14T17:59:15Z)
• Faster Video Moment Retrieval with Point-Level Supervision [70.51822333023145]
  Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: massive expensive temporal annotations and complicated cross-modal interaction modules. We propose a novel method termed Cheaper and Faster Moment Retrieval (CFMR)
  arXiv  Detail & Related papers  (2023-05-23T12:53:50Z)
• Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions [45.04514545004051]
  We provide convergence guarantees for score-based generative models (SGMs) We also examine SGMs based on the critically damped Langevin diffusion (CLD)
  arXiv  Detail & Related papers  (2022-09-22T17:55:01Z)
• Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method [23.62113376505929]
  This paper proposes a Generalized Power Method (GPM) to tackle the problem of community detection and group synchronization simultaneously. It is shown that the algorithm is able to exactly recover the ground truth in $O(n2n$) $log2n$) in a time of $3.5$.
  arXiv  Detail & Related papers  (2021-12-28T16:17:51Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)

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.