Effective L-Diversity Anonymization Algorithm Based On Improved Clustering

Mass data has been collected and released everyday, at the same time, the published data contains a lot of sensitive information related to individuals. K-anonymity privacy preserving mechanisms can prevent the disclosure of individual privacy information in the scenarios of data publication. L-diversity further considers the distribution of sensitive attributes in equivalence classes to avoid homogeneity attacks. In this paper, we propose an improved L-diversity algorithm based on clustering, and we consider the L-diversity demand of sensitive attributes while clustering to achieve K-anonymity. We minimize the total information loss of each equivalence class by choosing records which has minimal loss of information, regardless of whether they have different sensitive attributes, until the number of distinct values of sensitive attribute in the equivalence class reaches L. This algorithm we conduct experiments on UCI Adult data set and compared with traditional (K,L)-member algorithm. Theoretical analysis and the experimental results demonstrate that the improved L-diversity algorithm can not only improve the privacy protection degree of sensitive data, but also effectively reduce the information loss.