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 Decomposition+ an algorithm that implements some of these improvements and is thus more suited for use in real-life scenarios.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Das, D., Bhattacharyya, D.K. (2012). Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Computer Science and Engineering. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27308-7_44
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DOI: https://doi.org/10.1007/978-3-642-27308-7_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27307-0
Online ISBN: 978-3-642-27308-7
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