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A Micro-Aggregation Algorithm Based on Density Partition Method for Anonymizing Biomedical Data

[ Vol. 14 , Issue. 7 ]

Author(s):

Xiang Wu, Yuyang Wei, Tao Jiang, Yu Wang and Shuguang Jiang*   Pages 667 - 675 ( 9 )

Abstract:


Objective: Biomedical data can be de-identified via micro-aggregation achieving k - anonymity privacy. However, the existing micro-aggregation algorithms result in low similarity within the equivalence classes, and thus, produce low-utility anonymous data when dealing with a sparse biomedical dataset. To balance data utility and anonymity, we develop a novel microaggregation framework.

Methods: Combining a density-based clustering method and classical micro-aggregation algorithm, we propose a density-based second division micro-aggregation framework called DBTP . The framework allows the anonymous sets to achieve the optimal k- partition with an increased homogeneity of the tuples in the equivalence class. Based on the proposed framework, we propose a k − anonymity algorithm DBTP − MDAV and an l − diversity algorithm DBTP − l − MDAV to respond to different attacks.

Conclusions: Experiments on real-life biomedical datasets confirm that the anonymous algorithms under the framework developed in this paper are superior to the existing algorithms for achieving high utility.

Keywords:

Privacy protection, micro-aggregation, k − anonymity, l − diversity, clustering, biomedical data.

Affiliation:

School of Information and Electrical Engineering, China University Mining & Technology, Xuzhou, Jiangsu 221116, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, School of Information and Electrical Engineering, China University Mining & Technology, Xuzhou, Jiangsu 221116

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