Optimization algorithm for k-anonymization of datasets with low information loss

Anonymization is the modification of data to mask the correspondence between a person and sensitive information in the data. Several anonymization models such as k-anonymity have been intensively studied. Recently, a new model with less information loss than existing models was proposed; this is a type of non-homogeneous generalization. In this paper, we present an alternative anonymization algorithm that further reduces the information loss using optimization techniques. We also prove that a modified dataset is checked whether it satisfies the k-anonymity by a polynomial-time algorithm. Computational experiments were conducted and demonstrated the efficiency of our algorithm even on large datasets.