An Efficient and Dynamic Concept Hierarchy Generation for Data Anonymization

Protecting individual sensitive specific information has become an area of concern over the past one decade. Several techniques like k-anonymity and l-diversity employing generalization/suppression based on concept hierarchies (CHTS) were proposed in literature. The anonymization effectiveness depends on the CHT chosen from the various CHTS possible for a given attribute. This paper proposes a model for constructing dynamic CHT for numerical attributes which can be: 1) generated on the fly for both generalization/suppression; 2) dynamically adjusted based on a given k. The anonymized data using our method yielded 12% better utility when compared to existing methods. The results obtained after experimentation support our claims and are discussed in the paper.

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