Hybrid Filtrations Recommendation System based on Privacy Preserving in Edge Computing

被引：3
|
作者：
Ni, Lina [1 ,2 ]
Lin, Hongdi [1 ]
Zhang, Mengmeng [1 ]
Zhang, Jinquan [1 ]
机构：
[1] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China
[2] Tongji Univ, Minist Educ, Key Lab Embedded Syst & Serv Comp, Shanghai, Peoples R China
来源：
2017 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS | 2018年 / 129卷
关键词：
Privacy protection; edge computing; recommendation system; rough set theory; IOT APPLICATIONS; SECURE;
D O I：
10.1016/j.procs.2018.03.016
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. Copyright (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：407 / 409
页数：3
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