Performance Comparison of Collaborative Filtering with k-Anonymized Data by Fuzzy k-Member Clustering
Arina Kawano*, Katsuhiro Honda*, Akira Notsu*,
and Hidetomo Ichihashi**
*Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan
**Department of Economics, Osaka University of Economics and Law, 6-10 Gakuonji, Yao, Osaka 581-8511, Japan
In order to perform collaborative filtering with published databases in a privacy preserving manner, databases must be anonymized beforehand. This paper studies the applicability of fuzzy k-member clustering in privacy preserving collaborative filtering with k-anonymized data, in which users’ historical data of k or more users are suppressed considering soft data partitions. By allowing boundary samples to be shared by multiple clusters, data anonymization is performed without significant loss of information. Its performances are compared with several different types of fuzzy membership functions.
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