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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
