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Trust management model based on Intuitionistic Fuzzy Information in cloud environment

Published: 24 March 2021 Publication History

Abstract

Due to the subjectivity and ambiguity of users' trust ratings in the cloud environment, the existing trust management mechanism cannot express well the uncertainty of trust. This paper proposes a trust evaluation model based on intuitionistic fuzzy information, which firstly generates direct intuitionistic fuzzy information based on the user's own historical interaction information, and then removes abnormal recommendation information based on normal distribution for the recommendation information of the recommended user. Finally, the intuitionistic fuzzy information is integrated into the final decision information and the relative closeness degree with the optimal solution is calculated. Experiments show that the model has a higher interaction success rate than the classical algorithm for the recommendation of malicious services and malicious users.

References

[1]
Xu Zhongsheng, Shen Subin. 2015. A multi-objective optimization scheduling method for cloud computing resources. Microcomputers and applications, 34(13):17--20.
[2]
Lv Weifeng. 2012. Data as A Service in the Cloud Era. China Manufacturing Informatization, (18):48.
[3]
Shan Minghui. 2008. Research on key Technologies of Trust Management based on reputation. Doctoral Dissertation of Institute of Acoustics.
[4]
You Jing, Shangguan Jing Lun, Xu Shoukun, Li Qianmu, Wang Yinhai. 2017. Distributed Dynamic Trust Management Model considering trust reliability. Journal of Software, (9).
[5]
Liu Diamond, Geng Xiuli. 2017. Research on the Evaluation Method of Trusted Cloud Service based on D-S Theory. Computer Engineering and Applications, 53(017):70--76.
[6]
Xu Jiangke, Chang Zhaowen, Liang Min. 2012. A User behavior evaluation method based on Multi-entity Bayesian Network. Computer Applications and Software, 29(012):214--218.
[7]
Wang, Weize, Xinwang Liu. 2012. Intuitionistic fuzzy information aggregation using Einstein operations. IEEE Transactions on Fuzzy Systems 20.5 (2012): 923--938.
[8]
Huang Deshun. 2012. An Extended Study on the Algorithm of Intuitionistic Fuzzy Numbers and its Decision Model. Anhui University.
[9]
Xu, Zeshui, Cai, Xiaoqiang. 2012. Intuitionistic Fuzzy Information Aggregation.
[10]
prosthodontic. 2016. Subjective trust model with integrated intuitive fuzzy information. Computer application, 36(04):937--940+951.
[11]
Xu Jun, Zhong Yuansheng, Wan Shuping. 2016. An Incentive Adaptive trust Model integrating Intuitive Fuzzy Information. Electronics and informatics
[12]
Anderson, Marti J.2001. A new method for non -- parametric analysis of Variance. Austral Ecology 26.1 (2001): 32--46.

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EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2021

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Author Tags

  1. Cloud computing
  2. Intuitive fuzzy number
  3. Trust model

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  • Research-article
  • Research
  • Refereed limited

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EBIMCS 2020

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EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%

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