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We study the anonymization technique of k-anonymity family for preservingprivacy in the publication of microdata. Although existing approaches based ongeneralization can provide good enough protections, the generalized tablealways suffers from considerable information loss, mainly because thedistributions of QI (Quasi-Identifier) values are barely preserved and theresults of query statements are groups rather than specific tuples. To thisend, we propose a novel technique, called the Mutual Cover (MuCo), to preventthe adversary from matching the combination of QI values in publishedmicrodata. The rationale is to replace some original QI values with randomvalues according to random output tables, making similar tuples to cover foreach other with the minimum cost. As a result, MuCo can prevent both identitydisclosure and attribute disclosure while retaining the information utilitymore effectively than generalization. The effectiveness of MuCo is verifiedwith extensive experiments.

MuCo: Publishing Microdata with Privacy Preservation through Mutual Cover