A fuzzy variant of k-member clustering for collaborative filtering with data anonymization

@article{Honda2012AFV,
  title={A fuzzy variant of k-member clustering for collaborative filtering with data anonymization},
  author={Katsuhiro Honda and Arina Kawano and Akira Notsu and Hidetomo Ichihashi},
  journal={2012 IEEE International Conference on Fuzzy Systems},
  year={2012},
  pages={1-6}
}
Privacy preserving data mining is a promising approach for encouraging users to exploit the IT supports without fear of information leaks. k-member clustering is a basic technique for achieving k-anonymization, in which data samples are summarized so that any sample is indistinguishable from at least k - 1 other samples. This paper proposes a fuzzy variant of k-member clustering with the goal of improving the quality of data summarization with k-anonymity. Each k-member cluster is extracted… CONTINUE READING
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A study on privacy preserving collaborative filtering with data anonymization by clustering

  • K. Honda, Y. Matsumoto, A. Kawano, A. Notsu, H. Ichihashi
  • Proc. 5th KES International Conference on…
  • 2012
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