Improving Suppression to Reduce Disclosure Risk and Enhance Data Utility
This work addresses privacy-preserving data publishing for sensitive datasets, but it is incremental as it builds on existing suppression techniques.
The paper tackles the trade-off between privacy and data utility in data publishing by proposing an improved suppression method that targets high-risk records to reduce disclosure risk and enhance utility, demonstrating effectiveness on a real-world dataset.
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.