Jun Yan, Yihui Zhou, Laifeng Lu

A Node Differential Privacy-Based Method to Preserve Directed Graphs in Wireless Mobile Networks

  • Fluid Flow and Transfer Processes
  • Computer Science Applications
  • Process Chemistry and Technology
  • General Engineering
  • Instrumentation
  • General Materials Science
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With the widespread popularity of Wireless Mobile Networks (WMNs) in our daily life, the huge risk to disclose personal privacy of massive graph structure data in WMNs receives more and more attention. Particularly, as a special type of graph data in WMNs, the directed graph contains an amount of sensitive personal information. To provide secure and reliable privacy preservation for directed graphs in WMNs, we develop a node differential privacy-based method, which combines differential privacy with graph modification. In the method, the original directed graph is first divided into several sub-graphs after it is transformed into a weighted graph. Then, in each sub-graph, the node degree sequences are obtained by using an exponential mechanism and micro-aggregation is adopted to get the noised node degree sequences, which is used to generate a synthetic directed sub-graph through edge modification. Finally, all synthetic sub-graphs are merged into a synthetic directed graph that can preserve the original directed graph. The theoretical analysis proves that the proposed method satisfies differential privacy. The results of the experiments demonstrate the effectiveness of the presented method in privacy preservation and data utility.

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