BCSE:基于区块链的大数据可信服务评估模型

IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Fengyin Li;Xinying Yu;Rui Ge;Yanli Wang;Yang Cui;Huiyu Zhou
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引用次数: 18

摘要

区块链具有去中心化、持久性、匿名性和可审计性的关键特征,已成为克服传统公钥基础设施对第三方机构过度依赖和缺乏信任的解决方案。由于这些特性,区块链适用于解决面向服务的社交网络中的某些开放性问题,服务供应商提交的评论的不可靠性可能会导致严重的安全问题。为了解决提交评论的不可靠性问题,本文首先提出了一种基于区块链的身份认证方案和一种新的可信服务评估模型,并将该方案引入到服务评估模型中。新的可信服务评估模型由基于区块链的身份认证方案、评估提交模块和评估公示模块组成。在所提出的评估模型中,只有成功通过身份验证的用户才能向服务供应商提交评论。用户身份的注册和认证记录以及对服务供应商的审查都存储在区块链网络中。安全分析表明,该模型可以确保用户对服务供应商的评价的可信度,其他用户可以通过评价公示模块获得服务供应商的可信评价。实验结果还表明,与其他模型相比,所提出的模型具有更低的评审提交延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BCSE: Blockchain-based trusted service evaluation model over big data
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users' identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users' reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
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