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Deferentially private tagging recommendation based on topic model

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成果类型:
期刊论文、会议论文
作者:
Zhu, Tianqing;Li, Gang;Zhou, Wanlei;Xiong, Ping;Yuan, Cao
通讯作者:
Li, G.(gang.li@deakin.edu.au)
作者机构:
[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Information Technology, Deakin University, Australia
[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Mathematics and Computer, Wuhan Polytechnic University, China
[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Information, Zhongnan University of Economics and Law, China
语种:
英文
关键词:
Data mining;Background information;Differential privacies;Privacy concerns;Privacy preserving;Real-world datasets;Recommendation;Tagging;Weight perturbation;User interfaces
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2014
卷:
8443 LNAI
期:
PART 1
页码:
557-568
会议名称:
18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014
会议时间:
13 May 2014 through 16 May 2014
会议地点:
Tainan
出版者:
Springer Verlag
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 system allows Internet users to annotate resources with personalized tags and provides users the freedom to obtain recommendations. However, It is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive tags with only a little background information. This paper proposes a privacy preserving tagging release algorithm, PriTop, which is designed to protect users under the notion of differential privacy. The proposed PriTop algorithm includes three privacy preserving ope...

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