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.

我们提出了一种新的模型反演攻击方法，名为 HomoGMI 和 HeteGMI，旨在通过最大化目标 GNN 上的交叉熵损失和重构图上的第一和第二阶近似程度优化方法，在多个基准测试中的实验证明，该方法可以比竞争对手获得更好的性能。