A Secure Schema for Recommendation Systems
Asny P.A, Susanna M. Santhosh · International Journal on Cybernetics & Informatics · 2016
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.