Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Yanwei Xu,Jiguo Yu,Wanchun Dou,Lianyong Qi

doi:10.1155/2017/3437854

Abstract

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

Highlights

With the ever-increasing volume and variety of web services in various web-based communities, it becomes a challenging task to find the web services that a target user is really interested in [1,2,3]
In view of these two challenges, a novel privacypreserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is put forward in this paper
In view of the drawbacks of existing approaches, a novel privacy-preserving and scalable service recommendation approach based on SimHash, that is, SerRecSimHash, is proposed in this paper, to cope with the service recommendation problems in the distributed cloud environment

Summary

Introduction

With the ever-increasing volume and variety of web services in various web-based communities, it becomes a challenging task to find the web services that a target user is really interested in [1,2,3]. In this situation, various service recommendation techniques are introduced to alleviate the heavy burden on the service selection decisions of target users, for example, the well-adopted user-based Collaborative Filtering (i.e., UCF). In the age of IoT (Internet of Things), the quality data of various services are often monitored and collected by geographically distributed sensors and stored in different cloud platforms [5] In this situation, the historical service usage data are not centralized, but distributed. For the involved multiple cloud platforms, their volume of service quality data may become increasingly huge with updates over time, which leads to a frequent recalculation of user similarity and reduces the recommendation scalability significantly

Methods

Results

Conclusion