Efficient and Secure Data Sharing Scheme on Interoperable Blockchain Database

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kun Hao;Junchang Xin;Zhiqiong Wang;Zhongming Yao;Guoren Wang
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引用次数: 1

Abstract

Interoperable Blockchain Database (IBD) can enable users to execute transactions for sharing data stored in various blockchains maintained by different organizations or communities in a transparent manner. However, compared to traditional distributed databases, IBD can hardly provide high-level security and scalability, which are caused by many factors, such as system architecture, consensus protocol, and interactive pattern. Among them, the consensus protocol is the most critical factor, since the credibility of consensus nodes inside the corresponding blockchains are difficult to be guaranteed. Additionally, the consensus protocol directly affects the verification efficiency for given transactions in IBD. In this paper, we formally concern the problem of secure data sharing in IBD. We present a scheme named Hybridchain to execute transactions for sharing data securely and efficiently. We first propose a novel concept named Interoperable Consensus Group (ICG) which organizes a set of basic consensus nodes into a group, each of which is responsible for managing at least one local blockchain. Then, we present an interoperable cross-chains consensus protocol to achieve eventual consistency of blockchain transactions. We conduct extensive experiments, and the evaluation results show that our proposed approach achieves superior performance.
可互操作区块链数据库上高效安全的数据共享方案
可互操作的区块链数据库(IBD)可以使用户以透明的方式执行交易,以共享存储在不同组织或社区维护的各种区块链中的数据。然而,与传统的分布式数据库相比,IBD很难提供高水平的安全性和可扩展性,这是由系统架构、共识协议、交互模式等诸多因素造成的。其中,共识协议是最关键的因素,因为相应区块链内部共识节点的可信度很难得到保证。此外,共识协议直接影响IBD中给定交易的验证效率。本文正式讨论了IBD中数据的安全共享问题。我们提出了一种名为Hybridchain的方案来安全有效地执行交易以共享数据。我们首先提出了一个名为互操作共识组(ICG)的新概念,它将一组基本共识节点组织成一个组,每个组负责管理至少一个本地区块链。然后,我们提出了一个可互操作的跨链共识协议,以实现区块链事务的最终一致性。我们进行了大量的实验,评估结果表明我们提出的方法取得了优异的性能。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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