Social networks, patient networks, and email networks are all examples of graphs that can be studied to learn about information diffusion, community structure and different system processes; however, they are also all examples of graphs containing potentially sensitive information. While several anonymization techniques have been proposed for social network data publishing, they all apply the anonymization procedure on the entire graph. Instead, we propose a local anonymization algorithm that focuses on obscuring structurally important nodes that are not well anonymized, thereby reducing the cost of the overall anonymization procedure. Based on our experiments, we observe that we reduce the cost of anonymization by an order of magnitude while maintaining, and even improving, the accuracy of different graph centrality measures, e.g. degree and betweenness, when compared to another well known data publishing approach.
展开▼
