Anonymization with Worst-Case Distribution-Based Background Knowledge

TL;DR

This paper introduces a new anonymization algorithm that considers worst-case distribution-based background knowledge to enhance privacy protection while maintaining data utility.

Contribution

It is the first to address distribution-based background knowledge in the worst-case scenario for data anonymization.

Findings

The proposed algorithm effectively protects individual privacy.

The method preserves high data utility.

Empirical results demonstrate robustness against worst-case background knowledge.

Abstract

Background knowledge is an important factor in privacy preserving data publishing. Distribution-based background knowledge is one of the well studied background knowledge. However, to the best of our knowledge, there is no existing work considering the distribution-based background knowledge in the worst case scenario, by which we mean that the adversary has accurate knowledge about the distribution of sensitive values according to some tuple attributes. Considering this worst case scenario is essential because we cannot overlook any breaching possibility. In this paper, we propose an algorithm to anonymize dataset in order to protect individual privacy by considering this background knowledge. We prove that the anonymized datasets generated by our proposed algorithm protects individual privacy. Our empirical studies show that our method preserves high utility for the published data at the same time.