Clustering-based privacy preserving anonymity approach for table data sharing

Abstract

Government data sharing can effectively improve the efficiency and quality of government services and enhance the ability of providing government services. However, data sharing may bring the risk of citizen privacy leakage. It is a challenging problem on improving government governance and service levels when sharing government data while guaranteed citizens’ privacy. For the diversity types and complex attributes of government data, this paper proposes a cluster-based anonymous table data sharing privacy protection method (CATDS). Firstly, preprocessing the data table. According to the correlation degree between attributes, the clustering algorithm is used to divide the data attribute column to generate multiple tables. That can reduce the data dimension and improve the algorithm execution speed. Then clustering the table data using k-medoids clustering algorithm to generate a clustering result table that initially satisfies the ķ-anonymity requirement. That can reduce the next generalization degree and improve the data availability. Finally, anonymizing the resulting clusters through generalization technique to ensure the privacy of the shared data. By comparing the CATDS with the Incognito algorithm which is a classical ķ-anonymity algorithm, it is proved that the proposed algorithm can effectively reduce the amount of information loss and improve the availability of shared table data while protecting the private information of shared table data.