Preserving Privacy in Joining Recommender Systems
Chia-Lung Hsieh, Justin Z. Zhan, Deniel Zeng, Fei–Yue Wang · 2008
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.