Privacy Frost: A User-Oriented Data Anonymization Tool

A challenging task in privacy protection for public data is to realize an algorithm that generalizes a table according to requirements of a data user. In this paper, we propose an anonymization scheme for generating a k-anonymous and l-diverse table, and show evaluation results using three different tables. Our scheme is based on both top-down and bottom-up approaches for full-domain and partial-domain generalization, and the requirements are automatically incorporated into the generated table. The generated table meets user´s requirements and can be employed in the services provided by users without any modification or evaluation.