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Privacy-preserving topic model for tagging recommender systems

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成果类型:
期刊论文
作者:
Zhu, Tianqing;Li, Gang*;Zhou, Wanlei;Xiong, Ping;Yuan, Cao
通讯作者:
Li, Gang
作者机构:
[Zhou, Wanlei; Li, Gang; Zhu, Tianqing] Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Melbourne, Vic 3125, Australia.
[Xiong, Ping] Zhongnan Univ Econ & Law, Sch Informat & Secur Engn, Wuhan, Peoples R China.
[Yuan, Cao] Wuhan Polytech Univ, Sch Informat Technol, Wuhan, Peoples R China.
通讯机构:
[Li, Gang] D
Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Melbourne, Vic 3125, Australia.
语种:
英文
关键词:
Recommender systems;Background information;Differential privacies;Privacy preserving;Recommendation algorithms;Sensitive informations;Tagging systems;Topic Modeling;Weight perturbation;Privacy by design
期刊:
Knowledge and Information Systems
ISSN:
0219-1377
年:
2016
卷:
46
期:
1
页码:
33-58
机构署名:
本校为其他机构
院系归属:
数学与计算机学院
摘要:
Tagging recommender systems provide users the freedom to explore tags and obtain recommendations. The releasing and sharing of these tagging datasets will accelerate both commercial and research work on recommender systems. However, releasing the original tagging datasets is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from tagging datasets with only a little background information. Recently, several privacy techniques have been proposed to address the problem, bu...

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