A Hybrid Method For K-Anonymization Using Clustering Technique - Abd.Rahim, Y; Sahib, S.; Abd Ghani, M. K.; Yaacob, A.H (2011)
K-anonymity is a model to protect public released data from identification. These techniques have been focus of intense research in the last few years. An important requirement for such techniques is to minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality in k-anonymization model. This work compares the performance of two recently proposed clustering-based techniques for k-anonymization, and proposes a hybrid of both techniques to achieve less information loss than each of the original techniques. Based on results show that the proposed hybrid technique reduces not only the total information loss but also the variance of information loss among groups.