VertexSerum: Poisoning Graph Neural Networks for Link Inference
Ruyi Ding Shijin Duan Xiaolin Xu Yunsi Fei
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.