Towards privacy preserving social recommendation under personalized privacy settings

doi:10.1007/s11280-018-0620-z

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Towards privacy preserving social recommendation under personalized privacy settings
	Meng, Xuying 2; Wang, Suhang 3; Shu, Kai 3; Li, Jundong 3; Chen, Bo 1; Liu, Huan 3; Zhang, Yujun 2,4
刊名	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
	2019-11-01
卷号	22 期号:6 页码:2853-2881
关键词	Differential privacy Social recommendation Ranking Personalized privacy settings
ISSN号	1386-145X
DOI	10.1007/s11280-018-0620-z
英文摘要	Privacy leakage is an important issue for social relationships-based recommender systems (i.e., social recommendation). Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users' information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using personalized privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to achieve privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel privacy-preserving social recommendation framework, in which user can model user feedbacks and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive feedbacks, we can protect users' privacy against untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users' privacy while being able to retain effectiveness of the underlying recommender system.
资助项目	National Science Foundation of China[61672500] ; National Science Foundation of China[61572474] ; Program of International ST Cooperation[2016YFE0121500] ; National Science Foundation (NSF)[1614576] ; Office of Naval Research (ONR)[N00014-16-1-2257]
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000504322400026
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14982]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yujun
作者单位	1.Michigan Technol Univ, Dept Comp Sci, Houghton, MI 49931 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Arizona State Univ, Dept Comp Sci, Tempe, AZ 85287 USA 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Meng, Xuying,Wang, Suhang,Shu, Kai,et al. Towards privacy preserving social recommendation under personalized privacy settings[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2019,22(6):2853-2881.
APA	Meng, Xuying.,Wang, Suhang.,Shu, Kai.,Li, Jundong.,Chen, Bo.,...&Zhang, Yujun.(2019).Towards privacy preserving social recommendation under personalized privacy settings.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,22(6),2853-2881.
MLA	Meng, Xuying,et al."Towards privacy preserving social recommendation under personalized privacy settings".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 22.6(2019):2853-2881.