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Open Access

BCSE: Blockchain-Based Trusted Service Evaluation Model over Big Data

School of Computer Science, Qufu Normal University, Rizhao 276826, China
School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
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Abstract

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.

Keywords

social networktrusted service evaluation modelblockchainidentity authentication

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Big Data Mining and Analytics
Volume 5 Issue 1,
March 2022
Pages 1-14
DOI: 10.26599/BDMA.2020.9020028
Cite this article:
Li F, Yu X, Ge R, et al. BCSE: Blockchain-Based Trusted Service Evaluation Model over Big Data. Big Data Mining and Analytics, 2022, 5(1): 1-14. https://doi.org/10.26599/BDMA.2020.9020028

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Received: 05 August 2020
Revised: 30 September 2020
Accepted: 18 October 2020
Published: 27 December 2021
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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