Efficient K-anonymization for privacy preservation
Zhuang Liang, Ronglei Wei · 2008
Privacy preservation during cooperation has become an interesting issue in the last few years. This problem attracted much research work. k-anonymization is an efficient approach to protect data privacy. However, k-anonymization problem was proven NP-hard though the idea of k-anonymizafion is not complex. In this paper, we propose two simple but very efficient algorithms, which work for numeric and categorical data respectively, can minimize information loss as low as possible. We show that these algorithms can produce better performance comparing to other known algorithms.