Defense against membership inference attack in graph neural networks through graph perturbation.

Graph neural networks have demonstrated remarkable performance in learning node or graph representations for various graph-related tasks. However, learning with graph data or its embedded representations may induce privacy issues when the node representations contain sensitive or private user information. Although many machine learning models or techniques have been proposed for privacy preservation of traditional non-graph structured data, there is limited work to address graph privacy concerns. In this paper, we investigate the privacy problem of embedding representations of nodes, in which an adversary can infer the user's privacy by designing an inference attack algorithm. To address this problem, we develop a defense algorithm against white-box membership inference attacks, based on perturbation injection on the graph. In particular, we employ a graph reconstruction model and inject a certain size of noise into the intermediate output of the model, i.e., the latent representations of the nodes. The experimental results obtained on real-world datasets, along with reasonable usability and privacy metrics, demonstrate that our proposed approach can effectively resist membership inference attacks. Meanwhile, based on our method, the trade-off between usability and privacy brought by defense measures can be observed intuitively, which provides a reference for subsequent research in the field of graph privacy protection. [ABSTRACT FROM AUTHOR]