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

摘要

Objective: Biomedical data can be de-identified via micro-aggregation achieving k - anonymity privacy. However, the existing micro-aggregation algorithms result in low similaritywithin the equivalence classes, and thus, produce low-utility anonymous data when dealing witha sparse biomedical dataset. To balance data utility and anonymity, we develop a novel microaggregationframework.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 increasedhomogeneity of the tuples in the equivalence class. Based on the proposed framework, we proposea k ? anonymity algorithm DBTP ? MDAV and an l ? diversity algorithm DBTP ? l ? MDAV torespond to different attacks.Conclusions: Experiments on real-life biomedical datasets confirm that the anonymous algorithmsunder the framework developed in this paper are superior to the existing algorithms for achievinghigh utility.