(α, β, k)-anonymity: An effective privacy preserving model for databases
Yan Zhao, Jian Wang, Yongcheng Luo, Jiajin Le · 2009
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 paper, 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 microdata by using generalization and hierarchy. We verify (α, β, k)-anonymity approach can effectively protect privacy information of individual and resist background knowledge attack in publishing the data with multiple sensitive attributes by specific example.