Abstract
Online Social Networks (OSNs) have transformed the way that people socialize. However, when OSNs bring people convenience, privacy leakages become a growing worldwide problem. Although several anonymization approaches are proposed to protect information of user identities and social relationships, existing de-anonymization techniques have proved that users in the anonymized network can be re-identified by using an external reference social network collected from the same network or other networks with overlapping users. In this paper, we propose a novel social network de-anonymization mechanism to explore the impact of user attributes on the accuracy of de-anonymization. More specifically, we propose an approach to quantify diversities of user attribute values and select valuable attributes to generate the multipartite graph. Next, we partition this graph into communities, and then map users on the community level and the network level respectively. Finally, we employ a real-world dataset collected from Sina Weibo to evaluate our approach, which demonstrates that our mechanism can achieve a better de-anonymization accuracy compared with the most influential de-anonymization method.
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Acknowledgment
This work was partially supported by the US National Science Foundation under grant CNS-1704397, and the National Science Foundation of China under grants 61832012, 61771289, and 61672321.
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Zhang, C., Wu, S., Jiang, H., Wang, Y., Yu, J., Cheng, X. (2019). Attribute-Enhanced De-anonymization of Online Social Networks. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_29
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