Weiyi Zhong
Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment
Zhong, Weiyi; Yin, Xiaochun; Zhang, Xuyun; Li, Shancang; Dou, Wanchun; Wang, Ruili; Qi, Lianyong
Authors
Xiaochun Yin
Xuyun Zhang
Shancang Li
Wanchun Dou
Ruili Wang
Lianyong Qi
Abstract
© 2020 Elsevier B.V. With the advance of mobile edge computing (MEC), the number of edge services running on mobile devices grows explosively. In this situation, it is becoming a necessity to recommend the most suitable edge services to a mobile user from massive candidates, based on the historical quality of service (QoS) data. However, historical QoS is a kind of private data for users, which needs to be protected from privacy disclosure. Currently, researchers often use the Locality-Sensitive Hashing (LSH) technique to achieve the goal of privacy-aware recommendations. However, existing LSH-based methods are only applied to the recommendation scenarios with a single QoS dimension (e.g., response time or throughput), without considering the multi-dimensional QoS (e.g., response time and throughput) ensemble, which narrow the application scope of LSH in privacy-preserving recommendations significantly. Considering this drawback, this paper proposes a multi-dimensional quality ensemble-driven recommendation approach named RecLSH-TOPSIS based on LSH and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) techniques. First, the traditional single-dimensional LSH recommendation approach is extended to be a multi-dimensional one, through which we can obtain a set of candidate services that a user may prefer. Second, we use TOPSIS technique to rank the derived multiple candidate services and return the user an optimal one. At last, a case study is presented to illustrate the feasibility of our proposal to make privacy-preserving edge service recommendations with multiple QoS dimensions.
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 10, 2020 |
Online Publication Date | Apr 15, 2020 |
Publication Date | May 1, 2020 |
Deposit Date | Apr 16, 2020 |
Publicly Available Date | Apr 16, 2021 |
Journal | Computer Communications |
Print ISSN | 0140-3664 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 157 |
Pages | 116-123 |
DOI | https://doi.org/10.1016/j.comcom.2020.04.018 |
Keywords | Computer Networks and Communications |
Public URL | https://uwe-repository.worktribe.com/output/5872797 |
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Multi-Dimensional Quality-Driven Service Recommendation With Privacy-Preservation In Mobile Edge Environment
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Licence
http://creativecommons.org/licenses/by-nc-nd/4.0/
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http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
This is the author’s accepted manuscript. The published version can be found on the publishers website here: https://doi.org/10.1016/j.comcom.2020.04.018
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