Privacy Preservation Data Publishing Using Slicing

In earlier days we have techniques like Bucketization and Generalization for Privacy preservation. Since these two techniques have some disadvantages like, for high dimensional data we can’t use Generalization as it loses considerable amount of information. Also 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. Hence, to overcome above disadvantages, we are introducing new technique called as Slicing, which partitions the data both horizontally and vertically. 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. We show how slicing can be used for attribute disclosure protection. Our experiments confirm that slicing preserves better utility than generalization and is more effective than Bucketization involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.