Attribute-Enhanced De-anonymization of Online Social Networks
Attribute-Enhanced De-anonymization of Online Social Networks
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DOI：
10.1007/978-3-030-34980-6_29
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Cheng Zhang;Shang Wu;Honglu Jiang;Yawei Wang;Jiguo Yu;Xiuzhen Cheng
中科院分区：
文献类型：
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作者：
Cheng Zhang;Shang Wu;Honglu Jiang;Yawei Wang;Jiguo Yu;Xiuzhen Cheng
在线社交网络（OSN）已经改变了人们的社交方式。然而，在OSN给人们带来便利的同时，隐私泄露也成为一个日益严重的世界性问题。虽然提出了几种匿名化方法来保护用户身份和社交关系的信息，但现有的去匿名化技术已经证明，可以通过使用从相同网络或具有重叠用户的其他网络收集的外部参考社交网络来重新识别匿名化网络中的用户。在本文中，我们提出了一种新的社会网络去匿名化机制，探讨用户属性对去匿名化的准确性的影响。更具体地说，我们提出了一种方法来量化用户属性值的差异，并选择有价值的属性生成的多部图。接下来，我们将该图划分为社区，然后分别在社区级别和网络级别上映射用户。最后，我们使用从新浪微博收集的真实世界的数据集来评估我们的方法，这表明我们的机制可以实现更好的去匿名化的准确性相比，最有影响力的去匿名化方法。
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.