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. © 2011 ACM.