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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.

As medical data continues to transition to an electronic format, opportunities arise for researchers to use this microdata to discover patterns and increase knowledge in order to improve patient care. Now more than ever, it is critical to protect the identities of the patients contained in these databases. Even after removing obvious "identifier" attributes, such as social security numbers or first and last names, that clearly identify a specific person, it is possible to join "quasi-identifier" attributes from two or more publicly available databases to identify individuals. K-anonymity is an established approach that has been used to ensure that no one individual can be distinguished within a group of at least k individuals. The majority of the proposed approaches implementing k-anonymity have focused on improving the efficiency of algorithms implementing k-anonymity; less emphasis has been put towards ensuring the "utility" of anonymized data from a researchers' perspective. We propose a data utility measurement, called the research value (RV), which evaluates how well common cutoffs for numerical data or groupings in categorical data are preserved during the anonymization process. The proposed algorithm utilizing the new utility function scales efficiently when the number of attributes is large, while still ensuring that the generalization process is dictated by the data content expert's assessment of the utility of the generalized data.