Performance Comparison of Collaborative Filtering withk-Anonymized Data by Fuzzyk-Member Clustering
Arina Kawano, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2014
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 fuzzyk-member clustering in privacy preserving collaborative filtering withk-anonymized data, in which users’ historical data ofkor 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.