VertexSerum: Poisoning Graph Neural Networks for Link Inference
VertexSerum: Poisoning Graph Neural Networks for Link Inference
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DOI：
10.1109/iccv51070.2023.00418
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发表时间：
2023-08
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影响因子：
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通讯作者：
Ruyi Ding;Shijin Duan;Xiaolin Xu;Yunsi Fei
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文献类型：
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作者：
Ruyi Ding;Shijin Duan;Xiaolin Xu;Yunsi Fei
图神经网络（gnn）在利用图结构数据的各种应用中带来了优异的性能，如社会分析和欺诈检测。图链接，例如社会关系和交易历史，是敏感和有价值的信息，这在使用gnn时引起了隐私问题。为了利用这些漏洞，我们提出了一种新的图中毒攻击VertexSerum，它通过放大链接连接泄漏来提高图链接窃取的有效性。为了更准确地推断节点邻接性，我们提出了一种可以嵌入到链路检测网络中的注意机制。我们的实验表明，VertexSerum显著优于SOTA链接推理攻击，在四个真实数据集和三种不同的GNN结构中平均提高了9.8%的AUC分数。此外，我们的实验揭示了VertexSerum在黑盒和在线学习环境中的有效性，进一步验证了其在现实场景中的适用性。源代码可从https://github.com/RollinDing/VertexSerum获得。
Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network. Our experiments demonstrate that VertexSerum significantly outperforms the SOTA link inference attack, improving the AUC scores by an average of 9.8% across four real-world datasets and three different GNN structures. Furthermore, our experiments reveal the effectiveness of VertexSerum in both black-box and online learning settings, further validating its applicability in real-world scenarios. The source code is available at https://github.com/RollinDing/VertexSerum.