Asny P.A1and Susanna M. Santhosh
Mar Baselios Institute of Technology and Science, India

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This creates a serious privacy risk for the users. This paper aims to protect the private data against the service provider while preserving the functionality of the system. This paper provides a general framework that, with the help of a preprocessing phase that is independent of the inputs of the users, allows an arbitrary number of users to securely outsource a computation to two non-colluding external servers. This paper use these techniques to implement a secure recommender system based on collaborative filtering that becomes more secure, and significantly more efficient than previously known implementations of such systems.

Secure multi-party computation, privacy, recommender systems,secret sharing