YANA: an efficient privacy-preserving recommender system for online social communities

Authors:
Dongsheng Li;Qin Lv;Li Shang;Ning Gu
Affiliations:
Fudan University, Shanghai, China;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA;Fudan University, Shanghai, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

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. Serious privacy issues may arise in this process, as sensitive personal information (e.g., content interests) may be collected and disclosed to other parties, especially the recommender server. In this paper, we propose YANA (short for "you are not alone"), an efficient group-based privacy-preserving collaborative filtering system for content recommendation in online social communities. We have developed a prototype system on desktop and mobile devices, and evaluated it using real world data. The results demonstrate that YANA can effectively protect users' privacy, while achieving high recommendation quality and energy efficiency.