A Privacy Preserving Aggregation Scheme for Fog-Based Recommender System

With the rapid growth in the number of smart devices and explosive data generated every day by the mobile users, cloud computing comes to the bottleneck due to the far-off transmission and bandwidth limitation. Fog computing has been introduced as one of the promising solutions to meet the requirements under Internet of Things (IoT) scenarios such as location awareness and real-time services. The study of fog-based applications has become an attractive and important potential trend. The existing research about fog-based recommender systems focus on providing personalized and localized services to users while serving as a fog computing optimization tool in the system. However, there is little research about how to preserve user privacy in fog-based recommender systems. In this paper, we propose a novel privacy preserving aggregation scheme to handle the privacy issue for fog-based recommender systems.