  With the increasing popularity of graph neural networks (GNNs) in several
sensitive applications like healthcare and medicine, concerns have been raised
over the privacy aspects of trained GNNs. More notably, GNNs are vulnerable to
privacy attacks, such as membership inference attacks, even if only black-box
access to the trained model is granted. We propose PrivGNN, a
privacy-preserving framework for releasing GNN models in a centralized setting.
Assuming an access to a public unlabeled graph, PrivGNN provides a framework to
release GNN models trained explicitly on public data along with knowledge
obtained from the private data in a privacy preserving manner. PrivGNN combines
the knowledge-distillation framework with the two noise mechanisms, random
subsampling, and noisy labeling, to ensure rigorous privacy guarantees. We
theoretically analyze our approach in the Renyi differential privacy framework.
Besides, we show the solid experimental performance of our method compared to
several baselines adapted for graph-structured data. Our code is available at
https://github.com/iyempissy/privGnn.
