De-Anonymizing Social Networks With Random Forest Classifier

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Abstract
Personal privacy is facing severe threats as social networks are sharing user data with advertisers, application developers and data mining researchers. Although these data are anonymized by removing personal information, such as user identity, nickname or address information, personal information still could not be protected effectively. In order to arouse the attention of people from academia and industry for privacy protection, we propose a random forest method to de-anonymize social networks. First, we convert the social network de-anonymization problem into a binary classification problem between node pairs. In order to partition large sparse social networks, we use the spectral partition method to partition large graphs into a number of small subgraphs. And then we use the features of the network structure to train the random forest classifier. As a result, candidate node pairs from anonymous network and auxiliary network can be classified as matched pair by the random forest classifier. Furthermore, we improve the efficiency of our solution through parallelizing proposed method. The experiments conducted on the real datasets show that our solution’s Area Under the Curve (AUC) is 19% higher than baseline methods on average. Besides that, we test the robustness of the proposed algorithm by adding some noisy data, and the result demonstrates that our solution has good robustness.
Funding Information
  • National Nature Science Foundation of China (61402255, 61170292, 61373161, 61702462, 61672470)
  • National Key Research and Development projects intergovernmental cooperation in science and technology of China (12016YFE0100600, 12016YFE0100300)
  • Henan Province Science and Technology Department Foundation (162102410076, 162102310578)
  • Henan Province Educational Committee Foundation (16A520062, 17A520064)
  • Natural Science Foundation of Guangdong Province (2015A030310492)
  • Fundamental Research Project of Shenzhen Municipality (JCYJ20160301152145171)

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