A Novel Anonymity Algorithm for Privacy Preserving in Publishing Multiple Sensitive Attributes

Publishing the data with multiple sensitive attributes brings us greater challenge than publishing the data with single sensitive attribute in the area of privacy preserving. In this study, we propose a novel privacy preserving model based on k-anonymity called (α, β, k)-anonymity for databases. (α, β, k)- anonymity can be used to protect data with multiple sensitive attributes in data publishing. Then, we set a hierarchy sensitive attribute rule to achieve (α, β, k)-anonymity model and develop the corresponding algorithm to anonymize the micro data by using generalization and hierarchy. We also design experiments to show the application and performance of the proposed algorithm.