Improving the Utility of Anonymized Datasets through Dynamic Evaluation of Generalization Hierarchies

The dissemination of textual personal information has become a key driver for innovation and value creation. However, due to the possible content of sensitive information, this data must be anonymized, which can reduce its usefulness for secondary uses. One of the most used techniques to anonymize data is generalization. However, its effectiveness can be hampered by the Value Generalization Hierarchies (VGHs) used to dictate the anonymization of data, as poorly-specified VGHs can reduce the usefulness of the resulting data. To tackle this problem, we propose a metric for evaluating the quality of textual VGHs used in anonymization. Our evaluation approach considers the semantic properties of VGHs and exploits information from the input datasets to predict with higher accuracy (compared to existing approaches) the potential effectiveness of VGHs for anonymizing data. As a consequence, the utility of the resulting datasets is improved without sacrificing the privacy goal. We also introduce a novel rating scale to classify the quality of the VGHs into categories to facilitate the interpretation of our quality metric for practitioners.