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Private collaborative filtering under untrusted recommender server

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成果类型:
期刊论文
作者:
Xiong, Ping;Zhang, Lefeng;Zhu, Tianqing*;Li, Gang;Zhou, Wanlei
通讯作者:
Zhu, Tianqing
作者机构:
[Zhang, Lefeng; Xiong, Ping] Zhongnan Univ Econ & Law, Sch Informat & Secur Engn, Wuhan, Peoples R China.
[Zhu, Tianqing] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
[Zhou, Wanlei; Li, Gang; Zhu, Tianqing] Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Burwood, Vic 3125, Australia.
通讯机构:
[Zhu, Tianqing] W
Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
语种:
英文
关键词:
Recommender system;Collaborative filtering;Substitution;Differential privacy
期刊:
Future Generation Computer Systems
ISSN:
0167-739X
年:
2020
卷:
109
页码:
511-520
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61502362]; Humanities and Social Sciences Planning Project of the China Ministry of Education [19YJAZH099]
机构署名:
本校为通讯机构
院系归属:
数学与计算机学院
摘要:
Recommender systems play an increasingly vital role in modern E-commerce. However, exploiting users’ preferences with recommender algorithms leads to serious privacy risks, especially when recommender service providers are unreliable. To deal with the problem, this paper proposes a Client/Server framework to create a private recommender system (PrivateRS). The system assumes that the Server side is untrustworthy. On the Client side, each user firstly rates the items and randomizes the ratings with a differential privacy mechanism. The ratings ...

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