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Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment
Joint Authors
Qi, Lianyong
Dou, Wanchun
Xu, Yanwei
Yu, Jiguo
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-18
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user.
However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms.
Two challenges are present in such a cross-cloud service recommendation scenario.
First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely.
Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly.
In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper.
Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.
American Psychological Association (APA)
Xu, Yanwei& Qi, Lianyong& Dou, Wanchun& Yu, Jiguo. 2017. Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment. Complexity،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142714
Modern Language Association (MLA)
Xu, Yanwei…[et al.]. Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment. Complexity No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142714
American Medical Association (AMA)
Xu, Yanwei& Qi, Lianyong& Dou, Wanchun& Yu, Jiguo. Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment. Complexity. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142714
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142714