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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

Weiyi Zhong

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.

Citation

Zhong, W., Yin, X., Zhang, X., Li, S., Dou, W., Wang, R., & Qi, L. (2020). Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment. Computer Communications, 157, 116-123. https://doi.org/10.1016/j.comcom.2020.04.018

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
Electronic ISSN 1873-703X
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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