Paper ID: 2410.02826

LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN

Yuxing Zhang, Siyuan Meng, Chunchun Chen, Mengyao Peng, Hongyan Gu, Xinli Huang

Graph neural networks(GNNs) have a wide range of applications in this http URL 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 this http URL 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 this http URL 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 this http URL,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 this http URL theoretical analysis from the perspective of privacy theft,we first explore how to implement the aforementioned this http URL upon the findings,we design the Bridge Graph Generator to construct the Shadow-Target Bridge this http URL,the subgraph around the link is sampled by the Edge Subgraph Preparation this http URL,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 this http URL experiments validate the correctness of theoretical analysis and demonstrate that LinkThief still effectively steals links without extra assumptions.

Submitted: Oct 1, 2024