Set-valued data brings enormous opportunities to data mining tasks for various purposes. Many anonymous methods for set-valued data have been proposed to effectively protect an individual’s privacy against identify linkable attacks and item linkage attacks. In these methods, sensitive items are protected by a privacy threshold to limit the re-identification probability of sensitive items. However, lots of set-valued data have diverse sensitivity on data items. This leads to the over-protection problem when these existing privacy-preserving methods are applied to process the data items with diverse sensitivity, and it reduces the utility of data. In this paper, we propose a sensitivity-adaptive ρ-uncertainty model to prevent over-generalization and over-suppression by using adaptive privacy thresholds. Thresholds, which accurately capture the hidden privacy features of the set-valued dataset, are defined by uneven distribution of different sensitive items. Under the model, we develop a fine-grained privacy preserving technique through Local Generalization and Partial Suppression, which optimizes a balance between privacy protection and data utility. Experiments show that our method effectively improves the utility of anonymous data.
CITATION STYLE
Chen, L., Zhong, S., Wang, L. E., & Li, X. (2017). A sensitivity-adaptive ρ-uncertainty model for set-valued data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9603 LNCS, pp. 460–473). Springer Verlag. https://doi.org/10.1007/978-3-662-54970-4_27
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