Research Article
Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes
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@INPROCEEDINGS{10.1007/978-3-642-27308-7_44, author={Devayon Das and Dhruba Bhattacharyya}, title={Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes}, proceedings={Advances in Computer Science and Information Technology. Computer Science and Engineering. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part II}, proceedings_a={CCSIT PATR II}, year={2012}, month={11}, keywords={Privacy Preserving Data Publishing ℓ-diversity Decomposition Multiple Sensitive Attributes Multiple Release Publishing}, doi={10.1007/978-3-642-27308-7_44} }- Devayon Das
Dhruba Bhattacharyya
Year: 2012
Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes
CCSIT PATR II
Springer
DOI: 10.1007/978-3-642-27308-7_44
Abstract
In this paper, we analyse existing privacy-transformation techniques in the field of PPDP that anonymize datasets with Multiple Sensitive Attributes (MSA). Of these, we present an analysis of Decomposition, an algorithm which generates a dataset with distinct ℓ-diversity over MSA using a partitioning approach. We discuss some improvements which can be made over Decomposition: in the realms of its running time, its data utility, and its applicability in the case of Multiple Release Publishing. To this effect, we describe an algorithm that implements some of these improvements and is thus more suited for use in real-life scenarios.
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