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- W2938974329 abstract "Objective: Biomedical data can be de-identified via micro-aggregation achieving 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. Conclusion: 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." @default.
- W2938974329 created "2019-04-25" @default.
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- W2938974329 date "2019-09-17" @default.
- W2938974329 modified "2023-09-25" @default.
- W2938974329 title "A Micro-Aggregation Algorithm Based on Density Partition Method for Anonymizing Biomedical Data" @default.
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- W2938974329 doi "https://doi.org/10.2174/1574893614666190416152025" @default.
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