Advanced methods for generalizing time and duration during dataset anonymization
Jenno Verdonck, Kevin De Boeck, Michiel Willocx, Vincent Naessens · 2024
Time is an often recurring quasi-identifying attribute in many datasets. Anonymizing such datasets requires generalizing the time attribute(s) in the dataset. Examples are start dates and durations, which are traditionally generalized leading to intervals that do not embrace the relation between time attributes. This paper presents advanced methods for creating generalization hierarchies for time data. We propose clustering-based and Mondrian-based techniques to construct generalization hierarchies. These approaches take into account the relation between different time attributes and are designed to improve the utility of the anonymized data. We implemented these methods and conducted a set of experiments comparing them to traditional generalization strategies. The results show that our proposed methods improve the utility of the data for both statistical analysis and machine learning applications. Our approach demonstrates a significant increase in hierarchy quality and configuration flexibility, demonstrating the potential of our advanced techniques over existing methods.