SLOMS: A Privacy Preserving Data Publishing Method for Multiple Sensitive Attributes Microdata

Multi-dimension bucketization is a typical method to anonymize multiple sensitive attributes. However, the method leads to low data utility when microdata have more sensitive attributes. In addition, the methods do not generalize quasi-identifiers, which make the anonymous data vulnerable to suffer from linked attacks. To address the problems, the paper proposes a SLOMS method. The method vertically partitions the multiple sensitive attributes into several tables and bucketizes each sensitive attribute table to implement l-diversity. At the same time, it generalizes the quasi-identifiers to implement k-anonymity. The paper also proposes a MSB-KACA algorithm to anonymize microdata with multiple sensitive attributes by SLOMS. Experiments show that SLOMS can generate anonymous tables with less suppression ratio and less distortion compared with generalization and MSB.