An Enhanced Utility-Driven Data Anonymization Method

As medical data continues to transition to electronic formats, opportunities arise for researchers to use this microdata to discover patterns and increase knowledge that can improve patient care. We propose a data utility measurement, called the research value (RV), which reflects the importance of an database attribute with respect to the other database attributes in a dataset as well as reflect the significance of the content of the data from a researcher's point of view. Our algorithms use these research values to assess an attribute's data utility as it is generalizing the data to ensure k-anonymity. The proposed algorithms scale efficiently even when using datasets with large numbers of attributes.