Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

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 Rec_LSH-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.