  In Privacy Preserving Data Publishing, various privacy models have been
developed for employing anonymization operations on sensitive individual level
datasets, in order to publish the data for public access while preserving the
privacy of individuals in the dataset. However, there is always a trade-off
between preserving privacy and data utility; the more changes we make on the
confidential dataset to reduce disclosure risk, the more information the data
loses and the less data utility it preserves. The optimum privacy technique is
the one that results in a dataset with minimum disclosure risk and maximum data
utility. In this paper, we propose an improved suppression method, which
reduces the disclosure risk and enhances the data utility by targeting the
highest risk records and keeping other records intact. We have shown the
effectiveness of our approach through an experiment on a real-world
confidential dataset.
