A Novel Privacy Preserving Model for Datasets Re-Publication
Yan Zhao, Jian Wang, Qiu Sha Zhu, Jia Jin Le · Advanced materials research · 2010
Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles. In this paper, we first systematically characterize the inference attacks and set the hierarchy sensitive attribute rules. Then we propose a novel privacy preserving model based on k-anonymity for re-publication of multiple sensitive datasets and verify the novel approach that can eliminate inference channel and effectively protect privacy information in re-publication of datasets with multiple sensitive attributes by specific example. Finally, we present the anonymization algorithm to achieve privacy against inference attack.