Several anonymization techniques, like generalization and bucketization, have been intended for privacy preserving microdata publishing. current work has shown that generalization loses significant amount of information, particularly for high-dimensional data. on the other hand, Bucketization 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 present a new technique called slicing, in that data is partition into both horizontally and vertically. We demonstrate that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another main advantage of slicing is that it can handle high-dimensional data. We illustrate how slicing can be used for attribute disclosure protection and build up an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments verify that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also show that slicing can be used to prevent membership disclosure.
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