Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural
  Networks
Renyang Liu Wei Zhou Jinhong Zhang Xiaoyuan Liu Peiyuan Si Haoran Li
Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural
Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have
made remarkable progress in many physical scenarios, especially in
communication applications. Despite achieving great success, the privacy issue
of such models has also received considerable attention. Previous studies have
shown that given a well-fitted target GNN, the attacker can reconstruct the
sensitive training graph of this model via model inversion attacks, leading to
significant privacy worries for the AI service provider. We advocate that the
vulnerability comes from the target GNN itself and the prior knowledge about
the shared properties in real-world graphs. Inspired by this, we propose a
novel model inversion attack method on HomoGNNs and HeteGNNs, namely HomoGMI
and HeteGMI. Specifically, HomoGMI and HeteGMI are gradient-descent-based
optimization methods that aim to maximize the cross-entropy loss on the target
GNN and the $1^{st}$ and $2^{nd}$-order proximities on the reconstructed graph.
Notably, to the best of our knowledge, HeteGMI is the first attempt to perform
model inversion attacks on HeteGNNs. Extensive experiments on multiple
benchmarks demonstrate that the proposed method can achieve better performance
than the competitors.