13-11-2012, 02:08 PM
Slicing: A New Approach for Privacy Preserving Data Publishing
ABSTRACT
Several anonymization techniques, such as generalization
and bucketization, have been designed for privacy
preserving micro data publishing. Recent work has
shown that generalization loses considerable amount of
information, especially for high dimensional data.
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. In this paper, we
present a novel technique called slicing, which partitions
the data both horizontally and vertically. We show that
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 and develop an
efficient algorithm for computing the sliced data that
obey the ‘-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 demonstrate that slicing
can be used to prevent membership disclosure.