Privacy-preserving recommender systems in dynamic environments

Recommender systems play a crucial role today in on-line applications as they improve the customer satisfaction, and at the same time results in an increase in the profit for the service provider. However, there are serious privacy concerns as such systems rely on the personal data of the customers. There have been several proposals to provide privacy in recommender systems and, among many others, cryptographic techniques provide effective ways of protecting privacy-sensitive data of the customers. Unfortunately, existing methods only consider a static environment with constant number of customers in the system, which can be abused to extract more information on the customers when a cryptography based protocol is executed repeatedly. In this paper, we provide a privacy-preserving recommender system for a dynamic environment, which is more suitable for the real world applications.