MuCo: Publishing Microdata with Privacy Preservation through Mutual Cover

TL;DR

MuCo is a novel microdata anonymization method that replaces original quasi-identifier values with random ones to better preserve data utility while preventing privacy breaches.

Contribution

The paper introduces MuCo, a new anonymization technique that improves privacy protection and data utility compared to traditional generalization methods.

Findings

MuCo effectively prevents identity and attribute disclosure.

MuCo retains more data utility than existing generalization approaches.

Extensive experiments verify MuCo's effectiveness.

Abstract

We study the anonymization technique of k-anonymity family for preserving privacy in the publication of microdata. Although existing approaches based on generalization can provide good enough protections, the generalized table always suffers from considerable information loss, mainly because the distributions of QI (Quasi-Identifier) values are barely preserved and the results of query statements are groups rather than specific tuples. To this end, we propose a novel technique, called the Mutual Cover (MuCo), to prevent the adversary from matching the combination of QI values in published microdata. The rationale is to replace some original QI values with random values according to random output tables, making similar tuples to cover for each other with the minimum cost. As a result, MuCo can prevent both identity disclosure and attribute disclosure while retaining the information utility more effectively than generalization. The effectiveness of MuCo is verified with extensive experiments.