Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

Wenwen Gong,Lianyong Qi,Yanwei Xu

doi:10.1155/2018/3075849

Wenwen Gong, Lianyong Qi + Show 1 more

Open Access

https://doi.org/10.1155/2018/3075849

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Abstract

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

Highlights

With the advent of mobile computing age, an increasing number of web services are migrating to the mobile infrastructure or devices, producing a wide range of mobile services that cater for the mobile environment, such as mobile payment and mobile shopping [1,2,3]
Only one QoS dimension of mobile services is considered in the previous work, while, in this paper, we further extend our work by considering multiple quality dimensions simultaneously and further put forward a novel service recommendation approach that can protect user privacy over multiple QoS dimensions, named SerRecmulti-qos
Two quality dimensions of services are considered, that is, response time and another one whose QoS values are randomly generated according to the range of response time values ( WS-DREAM provides QoS data of two quality dimensions, response time and throughput, the QoS data distribution of throughput is very skew which makes it not suitable for Locality-Sensitive Hashing (LSH)-based approximate nearest neighbor (ANN) search very much according to the LSH theory)

Summary

Introduction

With the advent of mobile computing age, an increasing number of web services are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and wearable devices), producing a wide range of mobile services that cater for the mobile environment, such as mobile payment and mobile shopping [1,2,3] This rapid growth of number of mobile services, on one hand, provides more service candidates that can satisfy the users’ functional or nonfunctional requirements and, on the other hand, places a heavy burden on the users’ service selection decisions as selecting an appropriate mobile service is really a tiresome and time-consuming job [4, 5]. How to integrate or fuse these distributed service usage data across different fog platforms while guaranteeing user privacy has become a challenging task that needs further investigation

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