Abstract
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 \({\rho }\)-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.
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Acknowledgments
The research is supported by the National Science Foundation of China (Nos. 61272535, 61363009, 61365009, 61502111), Guangxi Bagui Scholar Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (Nos. 2015GXNSFBA139246, 2013GXNSFBA019263, 2014GXNSFBA118288), Science and Technology Research Projects of Guangxi Higher Education (Nos. 2013YB029, 2015YB032), the Guangxi Science Research and Technology Development Project (No. 14124004-4-11) ,Youth Scientific Research Foundation of Guangxi Normal University and Innovation Project of Guangxi Graduate Education (No. YCSZ2015104).
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Chen, L., Zhong, S., Wang, Le., Li, X. (2017). A Sensitivity-Adaptive \(\rho \)-Uncertainty Model for Set-Valued Data. In: Grossklags, J., Preneel, B. (eds) Financial Cryptography and Data Security. FC 2016. Lecture Notes in Computer Science(), vol 9603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_27
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DOI: https://doi.org/10.1007/978-3-662-54970-4_27
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