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Published in:
2012 | OriginalPaper | Chapter
A Study on Privacy Preserving Collaborative Filtering with Data Anonymization by Clustering
Authors : Katsuhiro Honda, Yui Matsumoto, Arina Kawano, Akira Notsu, Hidetomo Ichihashi
Published in: Intelligent Interactive Multimedia: Systems and Services
Publisher: Springer Berlin Heidelberg
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Collaborative filtering achieves personalized recommendation based on user collaboration. In this paper, how to preserve personal information in collaborative filtering is studied through several comparative experiments.
k
-anonymization is a standard method for guaranteeing personal privacy, in which data records are summarized so that any record is indistinguishable from at least (
k
– 1) other records. This study compares several clustering-based k-anonymization models in the context of collaborative filtering application.