Towards privacy preserving social recommendation under personalized privacy settings

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...

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Bibliographic Details
Published in:World wide web (Bussum) 2019-11, Vol.22 (6), p.2853-2881
Main Authors: Meng, Xuying, Wang, Suhang, Shu, Kai, Li, Jundong, Chen, Bo, Liu, Huan, Zhang, Yujun
Format: Article
Language:English
Subjects:
Computer Science
Database Management
Information Systems Applications (incl.Internet)
Leakage
Noise sensitivity
Operating Systems
Privacy
Recommender systems
Special Issue on Social Computing and Big Data Applications
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Summary: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.
ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-018-0620-z