1. Fine-Grain Perturbation for Privacy Preserving Data Publishing
- Author
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Rhonda Chaytor, Patricia L. Brantingham, and Ke Wang
- Subjects
Information privacy ,Fine grain ,business.industry ,Computer science ,Corruption ,media_common.quotation_subject ,Data_MISCELLANEOUS ,Human immunodeficiency virus (HIV) ,Data publishing ,Computer security ,computer.software_genre ,medicine.disease_cause ,Privacy preserving ,Information sensitivity ,medicine ,Electronic publishing ,Data mining ,business ,computer ,media_common - Abstract
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 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.
- Published
- 2009