Towards publishing recommendation data with predictive anonymization
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Privacy-preserving collaborative recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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 E-commerce 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.