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Towards efficient privacy-preserving collaborative recommender systems

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6 Author(s)
Zhan, J. ; Heinz Sch., Carnegie Mellon Univ., Pittsburgh, PA ; I-Cheng Wang ; Chia-Lung Hsieh ; Tsan-sheng Hsu
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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 by constructing an efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

Published in:

Granular Computing, 2008. GrC 2008. IEEE International Conference on

Date of Conference:

26-28 Aug. 2008