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

(E-pub Abstract Ahead of Print)

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

Journal Name: Current Bioinformatics

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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 achieve high utility, we propose a density-based second division micro-aggregation framework called DBTP, combining a density-based clustering method and classical micro-aggregation algorithm.

The framework allows the anonymous sets to achieve the optimal k-partition with an increased homogeneity of the tuples in the equivalence class. In addition, we propose a k-anonymity algorithm DBTP-MDAV and an l-diversity algorithm DBTP-l-MDAV to respond to different attacks. Experiments on real-life biomedical datasets confirm that the anonymous algorithms under the framework proposed in this paper are superior to the existing algorithms for achieving high utility.

Keywords: Privacy protection; micro-aggregation; k-anonymity; l-diversity

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/1574893614666190416152025
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