Abstract Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first present an optimal global-recoding method for the (α, k)-anonymity problem. Next we propose two scalable local-recoding algorithms which are both more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general cases.
Wong et al. REF proposed an (α, k)-anonymity model, which protected both identifications and relationships to sensitive information in data and proved that the optimal (α, k)anonymity problem is NP-hard problem.
(α, k)-anonymous data publishing
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems
Computer Science