Generative Graph based Model Inversion Attack on Graph Neural Network

Aiming at the privacy leakage risks of Graph Neural Networks (GNNs) in black-box scenarios, this paper proposes a Generation-Graph based Model Inversion Attack on GNN (GenG-MIA). By constructing a generative attack framework and integrating public knowledge distillation with structural optimization strategies, the proposed method effectively addresses challenges such as the high-dimensional sparsity of graph structure data, generative bias, and model collapse. GenG-MIA operates in two stages: first, during the public knowledge distillation stage, Wasserstein GAN is employed to train generators and discriminators on public datasets, enhancing the authenticity and diversity of generated graphs through a diversity loss term and introducing local/global discriminators to mitigate semantic gaps; second, in the structure revelation stage, potential vector projections are optimized to align with the feature space of the target model, thus recovering missing sensitive structures in training graphs. Experimental results show that GenG-MIA significantly outperforms existing methods in terms of attack accuracy and efficiency, enabling the efficient reconstruction of the topological structures of target training graphs and providing a new paradigm for privacy risk assessment of GNN models. This study further expands the application potential of generative attacks in complex graph data scenarios and offers theoretical references for privacy protection and model robustness design.