PrivacyFrost2: A efficient data anonymization tool based on scoring functions

Abstract

In this paper, we propose an anonymization scheme for generating a k-anonymous and l-diverse (or t-close) table, which uses three scoring functions, and we show the evaluation results for two different data sets. Our scheme is based on both top-down and bottom-up approaches for full-domain and partial-domain generalization, and the three different scoring functions automatically incorporate the requirements into the generated table. The generated table meets users’ requirements and can be employed in services provided by users without any modification or evaluation.