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.