A Novel Approach for Secure data Publishing with Membership Disclosure
|R.Sravani1, Kante.Ramesh2 and D.Venkatesh3
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In modern days, for various forms of prepared information include tabular, graph and item set of information, information anonymization techniques have been theme for research. In this paper we present regular review for many forms of several anonymization techniques like generalization and bucketization, have been intended for confidential preserving micro data publishing. Our hot work has presented that generalization lose needed amount of information, particularly for elevated-dimensional data. The hand over, bucketization does not protect member ship disclosure. Where slicing is a technique that preserve better data utility when compare to generalization an also protects member ship disclosure better than bucketization. This paper focus on effective method that can be used s long as better data usage and it can maintain high-dimensional data.