Duplication with Trapdoor Sensitive Attribute Values: A New Approach for Privacy Preserving Data Publishing

摘要

Privacy Preserving Data Publishing addresses the problem of publishing the data collected from data owners by the data holder or publisher such that personal sensitive information of the individual is preserved and the published data is highly useful. The anonymization techniques such as Generalization, Suppression, Swapping, Bucketization and Randomization suffers from either individual identity disclosure or the significant loss in information which reduces the usefulness of the data. In this paper, we present a novel Privacy Preserving Data Publishing scheme based on tuple duplication. We introduce the notion of Semantically Equivalent Attribute Values for sensitive attributes and Reputation Loss by Disclosure to hide the sensitive information of an individual in the published data. The Trapdoor Attribute Values for sensitive attributes are defined which helps in recovering the original dataset from the published dataset. We evaluate the proposed scheme with the existing attack models and show that our scheme counters those attacks. We define our own privacy criterion and show that the published data achieves the same. We assess the utility of the published data by using the existing utility metrics and our own defined utility metric. We show that the utility of the published data using the proposed sanitization mechanism is high.