A method for obtaining digital signatures and public-key cryptosystems
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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
NTRU: A Ring-Based Public Key Cryptosystem
ANTS-III Proceedings of the Third International Symposium on Algorithmic Number Theory
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Fully homomorphic encryption using ideal lattices
Proceedings of the forty-first annual ACM symposium on Theory of computing
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Privacy-preserving collaborative filtering on vertically partitioned data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Fully homomorphic encryption over the integers
EUROCRYPT'10 Proceedings of the 29th Annual international conference on Theory and Applications of Cryptographic Techniques
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
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In recommendation systems, a central host typically requires access to user profiles in order to generate useful recommendations. This access, however, undermines user privacy; the more information is revealed to the host, the more the user's privacy is compromised. In this paper, we propose a novel end-to-end encrypted recommendation mechanism which encrypts sensitive private data at the user end, without ever exposing plaintext private data to the host server. Unlike previously proposed privacy-preserving recommendation mechanisms, the data in this proposed system are lossless - a pivotal feature to many applications, e.g., in health informatics, business analytics, cyber security, etc. We achieve this goal by developing encrypted-domain polynomial ring homomorphism cryptographic algorithms to compute similarity of encrypted scores on the server, so that collaborative recommendations can be computed in the encryption domain and only an authorized person can decrypt the exact results. We also propose a novel key management system to make sure private information retrieval and recommendation computations can be executed in the encrypted domain in practice. Our experiments show that the proposed scheme offers robust security and lossless accurate recommendation, as well as high efficiency. Our preliminary results show the recommendation accuracy is 21% better than the existing statistical lossy privacy-preserving mechanisms based on random perturbation and user profile distribution. This new approach can potentially be applied to various data mining and cloud computing environments and significantly alleviates the privacy concerns of users.