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

Author(s): Xiang Wu, Yuyang Wei, Tao Jiang, Yu Wang, Shuguang Jiang*

Journal Name: Current Bioinformatics

Volume 14 , Issue 7 , 2019

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Graphical 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.

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Article Details

Year: 2019
Published on: 16 April, 2019
Page: [667 - 675]
Pages: 9
DOI: 10.2174/1574893614666190416152025
Price: $65

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