Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

中文翻译：

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异构社交网络中通过去匿名化和聚合导致的隐私泄漏

尽管代表了个性化、定位和推荐的一种有前途的方法，但来自多个社交网络的用户配置文件的聚合将不可避免地导致严重的隐私泄露问题。在本文中，我们提出了一种新颖的异构去匿名化方案（NHDS），旨在对异构社交网络进行去匿名化。NHDS 首先利用网络图结构显着减小候选集的大小，然后利用用户配置文件信息以高置信度识别正确的映射用户。对现实世界社交网络数据集的性能评估表明，NHDS 显着优于先前的方案。最后，我们基于四个真实世界的社交网络数据集对跨网络聚合引起的隐私泄漏进行了实证研究。我们的研究结果表明，39。通过去匿名化披露了 9% 以上的信息，去匿名化率为 84%。还检查了用户人口统计和兴趣的详细隐私泄漏，这证明了已识别的隐私泄漏问题的实用性。