Hybrid Filtrations Recommendation System based on Privacy Preserving in Edge Computing
Lina Ni, Hongdi Lin, Mengmeng Zhang, Jinquan Zhang · Procedia Computer Science · 2018
It is challenging to design a secure recommendation system on the Internet which can help users to select their favorite products as less privacy leaked as possible. In this paper, we present a hybrid filtrations recommendation system based on privacy preserving in edge computing (HFRS-PP), which can prevent the users’ privacy information from being leaked via the merits of edge computing in the process of computing and ensure the real-time, accuracy and stability of the query results. Particularly, we propose a privacy-preserving recommendation algorithm to obtain the desired results for the end users through hybrid filtrations. The filtration-rough set theory algorithm is given to distinguish the valid reviews from spam reviews for the next filtration.