An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
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
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Providing Naïve Bayesian Classifier-Based Private Recommendations on Partitioned Data
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Providing predictions on distributed HMMs with privacy
Artificial Intelligence Review
Privacy-preserving eigentaste-based collaborative filtering
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
Shared collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
Recommendation in the end-to-end encrypted domain
Proceedings of the 20th ACM international conference on Information and knowledge management
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
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Collaborative filtering (CF) systems are widely used by E-commerce sites to provide predictions using existing databases comprised of ratings recorded from groups of people evaluating various items, sometimes, however, such systems’ ratings are split among different parties. To provide better filtering services, such parties may wish to share their data. However, due to privacy concerns, data owners do not want to disclose data. This paper presents a privacy-preserving protocol for CF grounded on vertically partitioned data. We conducted various experiments to evaluate the overall performance of our scheme.