Description

The basic idea of slicing is to break the association cross columns, but to preserve the association within each column. This reduces the dimensionality of the data and preserves better utility than generalization and bucketization. Slicing preserves utility because it groups highly correlated attributes together, and preserves the correlations between such attributes. Slicing protects privacy because it breaks the associations between uncorrelated attributes, which are infrequent and thus identifying. Note that when the data set contains QIs and one SA, bucketization has to break their correlation; slicing, on the other hand, can group some QI attributes with the SA, preserving attribute correlations with the sensitive attribute. The key intuition that slicing provides privacy protection is that the slicing process ensures that for any tuple, there are generally multiple matching buckets. 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 existing work they developed a novel technique called slicing, which partitions the data both horizontally and vertically. To provide better data utility in proposed work the notion of overlapping slicing, which duplicates an attribute in more than one columns. This release more attributes correlations.