A New Approach Slicing for Micro Data Publishing

Abstract-More techniques, such as generalization and bucketization, have been introduced for privacy preserving micro data publishing. Recent tasks have cleared that generalization loses some amount of information, especially for large (high-dimensional) data. Bucketization, is the another technique, 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 this paper, we introduce technique called slicing, which partitions the data both horizontally and vertically. We represent that slicing preserves best 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 shown how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also describes that slicing can be used to prevent membership disclosure