Protecting Attribute Disclosure for High Dimensionality and Preserving Publishing of Microdata
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 13, Issue 4
Abstract
: Generalization and Bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes.In this paper, we present a novel technique called slicing, which partitions the data both horizontally andvertically. We show that slicing preserves better data utility than gen- eralization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an ef- ficient algorithm for computing the sliced data that obey the ℓ-diversity requirement.Our workload experiments confirm that slicing preserves better utility than generalization and is more e ective than bucketization in workloads involving the sensitive attribute. Our e xpe ri me nt s also demonstrate that slicing can be used to prevent membership disclosure.
Authors and Affiliations
Shaik. Raf
Keywords
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