Privacy Frost: A User-Oriented Data Anonymization Tool
Shinsaku Kiyomoto, Yutaka Miyake, Toshiaki Tanaka · 2011
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.