Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
A privacy-preserving collaborative filtering scheme with two-way communication
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
A toolbox for K-centroids cluster analysis
Computational Statistics & Data Analysis
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
YANA: an efficient privacy-preserving recommender system for online social communities
Proceedings of the 20th ACM international conference on Information and knowledge management
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With the explosive growth of online social communities and massive user-generated content, privacy-preserving recommender systems, which identify information of interest to individual users without disclosing personal interests to other parties, have become increasingly important. Collaborative filtering (CF), a widely used recommendation technique, recommends content that similar users have liked. As a result, CF-based recommender systems may expose sensitive personal interest information. This is demonstrated by a privacy attack model we present that targets online social communities. To solve this problem, we propose an interest group based privacy-preserving recommender system called Pistis. By identifying inherent item-user interest groups and separating users' private interests from their public interests, Pistis can make recommendations based on aggregated judgments of group members and local personalization, thus avoiding the disclosure of personal interest information. Pistis has been deployed and evaluated in an online social community with over 63,000 users, 20,000 daily posts, and 180,000 daily reads. Compared with two representative CF-based methods, our evaluation results demonstrate that Pistis achieves better performance in privacy preservation, recommendation quality, and efficiency.