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

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