A K-anonymous clustering algorithm based on the analytic hierarchy process

Abstract

To protect the privacy of users, tables generally must be anonymized before publication. All existing anonymous methods have deficiencies. They do not consider the differences in attributes, or the optimization of information loss and time efficiency. his paper proposes a new method called KACM to realize k-anonymity. This method is mainly used for hybrid tables. The calculation of the distance between records considers the connection between quasi-identifier attributes and sensitive attributes, their effect on the sensitive privacy, and the information loss during the anonymity process. In the clustering process, the records with the minimum distance are always selected to add, and the clustering is individually controlled according to k to realize the equalization division of the equivalence class and reduce the total amount of distance calculation. Finally, the validity and practicability of the method are proved using theory and experiment.