Fine-Grain Perturbation for Privacy Preserving Data Publishing

— Recent work [12] 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 anticorruption 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.

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Rhonda Chaytor, Ke Wang, Patricia Brantingham
• Keywords: Corruption Attacks, Data Mining, ICDM 2009, Sensitive Attribute, Sensitive Information