Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
IPSec: The New Security Standard for the Internet, Intranets, and Virtual Private Networks
IPSec: The New Security Standard for the Internet, Intranets, and Virtual Private Networks
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 Risks in Recommender Systems
IEEE Internet Computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
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
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
IEEE Transactions on Knowledge and Data Engineering
A secure multidimensional point inclusion protocol
Proceedings of the 9th workshop on Multimedia & security
An agent-based approach for privacy-preserving recommender systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A survey of homomorphic encryption for nonspecialists
EURASIP Journal on Information Security
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
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
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By offering personalized content to users, recommender systems have become a vital tool in e-commerce and online media applications. Content-based algorithms recommend items or products to users, that are most similar to those previously purchased or consumed. Unfortunately, collecting and storing ratings, on which content-based methods rely, also poses a serious privacy risk for the customers: ratings may be very personal or revealing, and thus highly privacy sensitive. Service providers could process the collected rating data for other purposes, sell them to third parties or fail to provide adequate physical security. In this paper, we propose technological mechanisms to protect the privacy of individuals in a recommender system. Our proposal is founded on homomorphic encryption, which is used to obscure the private rating information of the customers from the service provider. While the user's privacy is respected by the service provider, by generating recommendations using encrypted customer ratings, the service provider's commercially valuable item-item similarities are protected against curious entities, in turn. Our proposal explores simple and efficient cryptographic techniques to generate private recommendations using a server-client model, which neither relies on (trusted) third parties, nor requires interaction with peer users. The main strength of our contribution lies in providing a highly efficient solution without resorting to unrealistic assumptions.