A K-anonymity clustering algorithm based on the information entropy
Jianpei Zhang, Ying Zhao, Yue Yang, Jing Yang · 2014
Data anonymization techniques are the main way to achieve privacy protection, and as a classical anonymity model, K-anonymity is the most effective and frequently-used. But the majority of K-anonymity algorithms can hardly balance the data quality and efficiency, and ignore the privacy of the data to improve the data quality. To solve the problems above, by introducing the concept of “diameter” and a new clustering criterion based on the parameter of the maximum threshold of equivalence classes, we proposed a K-anonymity clustering algorithm based on the information entropy. The results of experiments showed that both the algorithm efficiency and data security are improved, and meanwhile the total information loss is acceptable, so the proposed algorithm has some practicability in application.