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arXiv 2004.01939cs.DCcs.DS
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基于VDF的自适应攻击者拜占庭协议和一致性下界

A Lower Bound for Byzantine Agreement and Consensus for Adaptive Adversaries using VDFs

Thaddeus Dryja, Quanquan C. Liu, Neha Narula

摘要:

大规模加密货币需要数百万参与者的参与,并支持数十亿美元的经济活动,这导致了二进制拜占庭协议(BBA)和共识的新工作线。新的工作旨在实现通信效率-鉴于这样一个大的$n$,不是每个人都可以在协议期间发言。几个协议已经取得了共识,即使在一个自适应的对手的通信效率,但他们需要额外的强有力的假设-工作证明,内存擦除等所有这些协议使用多播:每个诚实的副本多播消息到所有其他副本。在此模型下,我们提供了一个新的通信效率的共识协议,使用可验证的延迟函数(VDFs),是安全的自适应对手,不需要在其他协议中存在相同的强假设。一个自然的问题是,我们是否可以扩展的同步协议的部分同步设置-在这项工作中,我们表明,使用多播,我们不能。此外,我们不能实现始终安全的通信效率的协议(保持安全的概率为1),即使在同步设置对静态对手时,诚实的副本只选择组播其消息。考虑到这些不可能的结果,我们描述了一个新的通信效率的BBA协议在修改的部分同步网络模型,这是安全的自适应对手的概率很高。

英文摘要:

Large scale cryptocurrencies require the participation of millions of participants and support economic activity of billions of dollars, which has led to new lines of work in binary Byzantine Agreement (BBA) and consensus. The new work aims to achieve communication-efficiency---given such a large $n$, not everyone can speak during the protocol. Several protocols have achieved consensus with communication-efficiency, even under an adaptive adversary, but they require additional strong assumptions---proof-of-work, memory-erasure, etc. All of these protocols use multicast: every honest replica multicasts messages to all other replicas. Under this model, we provide a new communication-efficient consensus protocol using Verifiable Delay Functions (VDFs) that is secure against adaptive adversaries and does not require the same strong assumptions present in other protocols. A natural question is whether we can extend the synchronous protocols to the partially synchronous setting---in this work, we show that using multicast, we cannot. Furthermore, we cannot achieve always safe communication-efficient protocols (that maintain safety with probability 1) even in the synchronous setting against a static adversary when honest replicas only choose to multicast its messages. Considering these impossibility results, we describe a new communication-efficient BBA protocol in a modified partially synchronous network model which is secure against adaptive adversaries with high probability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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