Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
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
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
The effect of correlation coefficients on communities of recommenders
Proceedings of the 2008 ACM symposium on Applied computing
MobiRate: making mobile raters stick to their word
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
Proceedings of the third ACM conference on Recommender systems
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
A game theoretic framework for data privacy preservation in recommender systems
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Shared collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
Distributed rating prediction in user generated content streams
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
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
Proceedings of the 34th International Conference on Software Engineering
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
Proceedings of the 7th ACM conference on Recommender systems
A novel Bayesian similarity measure for recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Collaborative filtering has become an established method to measure users' similarity and to make predictions about their interests. However, prediction accuracy comes at the cost of user's privacy: in order to derive accurate similarity measures, users are required to share their rating history with each other. In this work we propose a new measure of similarity, which achieves comparable prediction accuracy to the Pearson correlation coefficient, and that can successfully be estimated without breaking users' privacy. This novel method works by estimating the number of concordant, discordant and tied pairs of ratings between two users with respect to a shared random set of ratings. In doing so, neither the items rated nor the ratings themselves are disclosed, thus achieving strictly-private collaborative filtering. The technique has been evaluated using the recently released Netflix prize dataset.