Privacy-Preserving Data Publishing via Mutual Cover

by   Boyu Li, et al.

We study anonymization techniques for preserving privacy in the publication of microdata tables. Although existing approaches based on generalization can provide enough protection for identities, anonymized tables always suffer from various attribute disclosures because generalization is inefficient to protect sensitive values and the partition of equivalence groups is directly shown to the adversary. Besides, the generalized table also suffers from serious information loss because the original Quasi-Identifier (QI) values are hardly preserved and the protection against attribute disclosure often causes over-protection against identity disclosure. To this end, we propose a novel technique, called mutual cover, to hinder the adversary from matching the combination of QI values in microdata tables. The rationale is to replace the original QI values with random QI values according to some random output tables that make similar tuples to cover for each other at the minimal cost. As a result, the mutual cover prevents identity disclosure and attribute disclosure more effectively than generalization while retaining the distribution of original QI values as far as possible, and the information utility hardly decreases when enhancing the protection for sensitive values. The effectiveness of mutual cover is verified with extensive experiments.


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