Abstract
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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Adusumalli, S.K., Valli Kumari, V. (2013). An Efficient and Dynamic Concept Hierarchy Generation for Data Anonymization. In: Hota, C., Srimani, P.K. (eds) Distributed Computing and Internet Technology. ICDCIT 2013. Lecture Notes in Computer Science, vol 7753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36071-8_39
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DOI: https://doi.org/10.1007/978-3-642-36071-8_39
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