Resumen
Every day data is published of different types andfrom various sources. Data de-identification protects the privacyof most of this data before its publication. Over the recent years,a technique proposed by Dr. Sweeney, known as kanonymizationas a means for privacy protection has gainedgreat popularity. There has been intensive research involving thismethod and many alterations, in the hope to find an optimalsolution in real-time to the generalization problem. To achieveeither generalization or suppression, researchers have useddifferent types of heuristics, most of them being tree-based.Although this is a heavily investigated area, there is no simplemethod to prepare data for generalization; in theory, there areinfinite methods for data preparation and partitioning. In thisresearch, we first propose the use of commonly known algorithmsto prepare data and achieve generalization. We then introducethe use of tree-based algorithms and tree traversal as themechanism to achieve data generalization. We further investigatethem, by comparing the quality of generalization sets obtained ineach traversal method, in the hope to determine which method isbest.