Fine-Grain Perturbation for Privacy Preserving Data Publishing

Recent work shows that conventional privacy preserving publishing techniques based on anonymity-groups are susceptible to corruption attacks. In a corruption attack, if the sensitive information of any anonymity-group member is uncovered, then the remaining group members are at risk. In this study, we abandon anonymity-groups and hide sensitive information through perturbation on the sensitive attribute. With each record being perturbed independently, corruption attacks cannot be effectively carried out. Previous anti-corruption work did not minimize information loss. This paper proposes to address this issue by allowing fine-grain privacy specification. We demonstrate the power of our approach through experiments on real medical and synthetic datasets.