  Graph Neural Networks (GNNs) have established themselves as the
state-of-the-art models for many machine learning applications such as the
analysis of social networks, protein interactions and molecules. Several among
these datasets contain privacy-sensitive data. Machine learning with
differential privacy is a promising technique to allow deriving insight from
sensitive data while offering formal guarantees of privacy protection. However,
the differentially private training of GNNs has so far remained under-explored
due to the challenges presented by the intrinsic structural connectivity of
graphs. In this work, we introduce differential privacy for graph-level
classification, one of the key applications of machine learning on graphs. Our
method is applicable to deep learning on multi-graph datasets and relies on
differentially private stochastic gradient descent (DP-SGD). We show results on
a variety of synthetic and public datasets and evaluate the impact of different
GNN architectures and training hyperparameters on model performance for
differentially private graph classification. Finally, we apply explainability
techniques to assess whether similar representations are learned in the private
and non-private settings and establish robust baselines for future work in this
area.
