Preserving Local Differential Privacy in Online Social Networks

Abstract

Following the trend of Online Social Networks (OSNs) data sharing and publishing, researchers raise their concern about the privacy problem. Differential privacy is such a mechanism to anonymize sensitive data. It deploys graph abstraction models, such as the Hierarchical Random Graph (HRG) model, to extract graph features. However, the injected noise amount, determined by the sensitivity, is usually proportion to the size of the whole network. Therefore, achieving global differential privacy may harm the utility of the releasing graphs.