AN EVALUATION OF DATA ANONYMIZATION METHODS FOR DATA PUBLISHING

Abstract

Data anonymization approaches have been the focus of research recently for several types of structured data, including tabular, graph, and item set data. In this article, we provide a succinct yet thorough assessment of a number of anonymization approaches, including generalisation and bucketization, which have been created for publishing microdata while protecting privacy. Recent research has demonstrated that generalisation results in significant information loss, particularly for high-dimensional data. Bucketization, however, does not stop membership disclosure. While slicing both prevents membership disclosure and preserves the data’s superior utility than generalisation. The practical methods that can be employed to provide improved data utility and handle high-dimensional data are the main emphasis of this research.