mproving the implementation of new approach forData Privacy Preserving in Data Mining using slicing
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 3
Abstract
Some different anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data 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 and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data.
Authors and Affiliations
Ravindra S
Keywords
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