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In the E-commerce era, recommender system is introduced to share customer experience and comments. At the same time, there is a need for Ecommerce entities to join their recommender system databases to enhance the reliability toward prospective customers and also to maximize the precision of target marketing. However, there will be a privacy disclosure hazard while joining recommender system databases. In order to preserve privacy in merging recommender system databases, we design a novel algorithm based on ElGamal scheme of homomorphic encryption.

Recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards that this paper addresses byconstructing an efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

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