【Author】
Yang, Dana; Doh, Inshil; Chae, Kijoon
【Abstract】Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR (Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.
【Keywords】Blockchains; Servers; Consensus algorithm; Collaborative work; Internet of Things; Throughput; Smart devices; Data science; Smart service; blockchain; consensus algorithm; raft algorithm; federated learning
【标题】基于单元格的 Raft 算法优化智能数据市场区块链上的共识过程
【摘要】由于物联网设备和流量的爆炸式增长，大数据正在发展为智能数据，通过公共空间用户的移动性和资源应用之间的关系，帮助数据科学专家了解人类活动。例如，智能数据市场有助于预测犯罪或了解 COVID-19 感染的原因。对于这些智能服务，用户同意隐私政策，以便第三方收集个人和敏感信息。但隐私政策的条件并没有规定是否可以跟踪用户的信息。为了确保数据透明性，许多系统都在使用带有 raft 算法的联盟/私有区块链。 raft 算法要求节点检查单个事务的无数消息。最终，随着节点数量的增加，整个系统的退化源于领导节点的负担。本文提出了一种通过将一定数量的交易划分为单元来处理收集到的交易的方法，无需任何额外的协议。所提出的方案还使用具有高精度和数据隐私的联邦学习模型，以确定区块链系统中的优化单元大小，从而在多个服务器上达成共识。因此，本文提出的 CBR（Cell-based Raft）共识算法提出了一种在不干扰现有 raft 算法概念的情况下减少消息数量的协议，以在海量交易发生的智能数据市场中保持稳定的吞吐量。
【关键词】区块链；服务器；共识算法；协作工作；物联网;吞吐量；智能设备；数据科学；智能服务；区块链；共识算法；筏算法；联合学习
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