LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
<|reference_start|>LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN: Graph neural networks(GNNs) have a wide range of applications in multimedia.Recent studies have shown that Graph neural networks(GNNs) are vulnerable to link stealing attacks,which infers the existence of edges in the target GNN's training graph.Existing attacks are usually based on the assumption that links exist between two nodes that share similar posteriors;however,they fail to focus on links that do not hold under this assumption.To this end,we propose LinkThief,an improved link stealing attack that combines generalized structure knowledge with node similarity,in a scenario where the attackers' background knowledge contains partially leaked target graph and shadow graph.Specifically,to equip the attack model with insights into the link structure spanning both the shadow graph and the target graph,we introduce the idea of creating a Shadow-Target Bridge Graph and extracting edge subgraph structure features from it.Through theoretical analysis from the perspective of privacy theft,we first explore how to implement the aforementioned ideas.Building upon the findings,we design the Bridge Graph Generator to construct the Shadow-Target Bridge Graph.Then,the subgraph around the link is sampled by the Edge Subgraph Preparation Module.Finally,the Edge Structure Feature Extractor is designed to obtain generalized structure knowledge,which is combined with node similarity to form the features provided to the attack model.Extensive experiments validate the correctness of theoretical analysis and demonstrate that LinkThief still effectively steals links without extra assumptions.<|reference_end|>
@article{zhang2024linkthief:,
    title={LinkThief: Combining Generalized Structure Knowledge with Node
  Similarity for Link Stealing Attack against GNN},
    author={Yuxing Zhang, Siyuan Meng, Chunchun Chen, Mengyao Peng, Hongyan Gu and
  Xinli Huang},
    journal={arXiv preprint arXiv:2410.02826},
    year={2024},
    doi={10.1145/3664647.3681381},
    archivePrefix={arXiv},
    eprint={2410.02826},
    primaryClass={cs.CR cs.AI cs.LG}
}