Slicing : A Efficient Method For Privacy Preservation In Data Publishing

In this paper we propose and prove a new technique called “Overlapping Slicing” for privacy preservation of high dimensional data. The process of publishing the data in the web, faces many challenges today. The data usually contains the personal information which are personally identifiable to anyone, thus poses the problem of Privacy. Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. Recent work in data publishing information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure. We propose an overlapping slicing method for handling high into more than one column; we protect privacy by breaking the association of uncorrelated attributes and preserve data utility by preserving the association between highly correlated attributes. This technique releases mo correlations thereby, overlapping slicing preserves better data utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute