An automated data utility clustering methodology using data constraint rules

Many data privacy models have been created in the last few years using the k-anonymization methodology including l-diversity, p-sensitive k-anonymity, and t-closeness. While these methods differ in their approaches and quality of the results, they all focus on ensuring the anonymization of the data while at the same time attempt to protect the quality of the data by minimizing the loss of the information contained in the original data set. In this paper, we propose an automated k-anonymity approach that uses clustering to maximize the utility of the data while ensuring that the data privacy is maintained. Our method employs data constraint rules, which are defined by the data research expert to represent especially informative distributions in categorical attributes or inflections points in a continuous attribute. The values of the data constraints are an integral component of our utility function, which is used to maximize the utility of the anonymized dataset. Finally, we present our experimental results that show that our approach meets or exceeds existing methods that do not incorporate data constraint rules.