A Study on Privacy Preserving Collaborative Filtering with Data Anonymization by Clustering

  • Katsuhiro Honda
  • Yui Matsumoto
  • Arina Kawano
  • Akira Notsu
  • Hidetomo Ichihashi
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Katsuhiro Honda
    • 1
  • Yui Matsumoto
    • 1
  • Arina Kawano
    • 1
  • Akira Notsu
    • 1
  • Hidetomo Ichihashi
    • 1
  1. 1.Osaka Prefecture UniversitySakaiJapan

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