An Efficient Generalized Clustering Method for Achieving K-Anonymization

This paper proposed an efficient generalized clustering method which derives from the k-means algorithm for achieving k-anonymization with good data quality and minimum information loss. We defined the distance function for the three major attribute types: numerical type, categorical type, and structural type. Then we proceeded the method in two stages: preprocessing stage and postprocessing stage. The preprocessing stage is to partitions all records into[n/k] groups, and then add the records that are naturally similar to each other into every group. The postprocessing stage is to add each remaining record into a cluster with respect to which the increment of the information loss is minimal. We experimentally compared our method with other two clustering-based k-anonymization methods. The experiment showed that our method outperforms their method and also ensures the anonymization of data.