Dynamic Approach for Secure Data Publishing in Mining

More than a few anonymization techniques, such as simplification and bucketization, have been deliberate for privacy protecting micro data publishing. Current work has shown that generalization loses substantial quantity of in sequence, particularly for high-dimensional data. Bucketization, on the other offer, does not put off membership revelation and does not apply for data that do not have a clear partition between quasirecognizing attributes and perceptive attributes. In this nearby a novel technique called slicing, which partitions the information both horizontally and vertically? We demonstrate that slicing conserves better data effectiveness than simplification and can be used for association revelation protection. Another significant benefit of slicing is that it can handle high-dimensional data. Slicing can be used for attribute revelation protection and develop an efficient algorithm for computing the sliced data that go behind the l-diversity requirement. The workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads connecting the sensitive attribute. Our research also demonstrates that slicing can be used to prevent membership disclosure.