Abstract
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.
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Honda, K., Matsumoto, Y., Kawano, A., Notsu, A., Ichihashi, H. (2012). A Study on Privacy Preserving Collaborative Filtering with Data Anonymization by Clustering. In: Watanabe, T., Watada, J., Takahashi, N., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia: Systems and Services. Smart Innovation, Systems and Technologies, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29934-6_5
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DOI: https://doi.org/10.1007/978-3-642-29934-6_5
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