Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/78465
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Type: Conference paper
Title: Identifying fake feedback for effective trust management in cloud environments
Author: Noor, T.
Sheng, Q.
Alfazi, A.
Law, J.
Ngu, A.
Citation: Service-Oriented Computing - ICSOC 2012 Workshops: ICSOC 2012, International Workshops ASC, DISA, PAASC, SCEB, SeMaPS, WESOA, and Satellite Events, Shanghai, China, November 12-15, 2012, Revised Selected Papers / A. Ghose, H. Zhu, Qi Yu... et al. (eds.): pp.47-58
Publisher: Springer Berlin Heidelberg
Publisher Place: Berlin
Issue Date: 2013
Series/Report no.: Lecture Notes in Computer Science ; 7759
ISBN: 9783642378034
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Service-Oriented Computing (10th : 2012 : Shanghai, China)
Statement of
Responsibility: 
Talal H. Noor, Quan Z. Sheng, Abdullah Alfazi, Jeriel Law, Anne H. H. Ngu
Abstract: Managing trust in cloud environments is emerging as an important issue in recent years. The highly dynamic, distributed, and non-transparent nature of cloud services makes the trust management of these services difficult. Malicious users may collude to give multiple misleading trust feedback to disadvantage a cloud service, or create several accounts and then leave misleading trust feedback to trick users into trusting cloud services that are not actually trustworthy. In this paper, we propose techniques enabling the identification of fake trust feedbacks and thus provide significant improvement on trust management in cloud environments. In particular, we introduce a credibility model that not only identifies credible trust feedbacks from fake ones, but also preserves the privacy of cloud service consumers. The techniques have been validated by a prototype system implementation and experimental studies. © Springer-Verlag 2013.
Keywords: Trust management
cloud computing
credentials
credibility
reputation
security
privacy
Rights: © Springer-Verlag Berlin Heidelberg 2013
DOI: 10.1007/978-3-642-37804-1_6
Published version: http://dx.doi.org/10.1007/978-3-642-37804-1_6
Appears in Collections:Aurora harvest
Computer Science publications

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