论文标题
建模网络连接对工作证明区块链共识安全性的影响
Modeling the Impact of Network Connectivity on Consensus Security of Proof-of-Work Blockchain
论文作者
论文摘要
区块链是受欢迎的比特币背后的技术,被认为是“设计”系统的“安全性”,因为它旨在在没有中央信任的一组不信任的各方之间建立安全性。区块链的安全依赖于诚实多数的前提,即,只要大多数共识投票权是诚实的,区块链系统就被认为是安全的。在工作证明(POW)区块链的情况下,对手不能控制网络总计算能力的50%以上。但是,这个50%的阈值仅基于对计算能力的分析,对网络和节点行为具有隐式和理想主义的假设。最近的研究表明,诸如网络连通性,存在区块叉的存在以及采矿策略等因素可能会破坏诚实多数的共识安全性,但均未提供具体分析和定量评估。在本文中,我们通过提出一个分析模型来评估网络连通性对不同对手模型下POW区块链共识安全的影响来填补空白。我们将分析模型应用于两个对抗性场景:1)诚实但倾斜的倾向,2)自私采矿。对于每种情况,我们都会量化参与分叉种族中涉及的节点的通信能力,并估算对手的采矿收入及其对共识协议安全性的影响。仿真结果证实了我们的分析。我们的建模和分析为评估分布式共识系统中各种因素的安全影响提供了范式。
Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.