Efficient privacy-preserving content recommendation for online social communities

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• 作者：Dongsheng Li^a ; ^b ; ^dAuthor Vitae ; Qin Lv^c ; ^{qin.lv@colorado.edu}Author Vitae ; Li Shang^b ; ^cAuthor Vitae ; Ning Gu^a ; ^d ; ^{ninggu@fudan.edu.cn}Author Vitae
• 关键词：Recommendation ; Privacy ; Efficiency
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：219
• 期：Complete
• 页码：440-454
• 全文大小：1267 K
• 卷排序：219

文摘

In online social communities, many recommender systems use collaborative filtering, a method that makes recommendations based on what are liked by other users with similar interests. Privacy issues arise in this process, as sensitive personal information (e.g., content interests) may be collected and disclosed to the recommender server. Existing privacy-preserving collaborative filtering techniques trade either efficiency or accuracy for privacy, which are not suitable for online social communities with large amount of users. In this paper, we propose YANA (short for “you are not alone”), a user group-based privacy-preserving recommender system for users in online social communities. In this system, users are organized into groups with diverse interests and interact with the recommender server via interest-specific pseudo users, so that individual user's personal interest information remains hidden from the server. A suit of secure multi-party computation protocols and recommendation strategies are proposed to protect user privacy from group members in the recommendation process. A prototype system has been implemented on both mobile devices and desktop computers, and evaluation using real-world data demonstrates that YANA can effectively protect users’ privacy, while achieving high recommendation quality and energy efficiency.