Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards Privacy Compliant and Anytime Recommender Systems
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Privacy-preserving collaborative recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Secure personalized recommendation system for mobile user
ICISC'10 Proceedings of the 13th international conference on Information security and cryptology
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
P3ERS: Privacy-Preserving PEer Review System
Transactions on Data Privacy
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
Privacy-preserving matrix factorization
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
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Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private their personal information, including their buying preferences, and they should not be tracked against their will. The commercial interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between these two parties.