  In recent years, the fast rise in number of studies on graph neural network
(GNN) has put it from the theories research to reality application stage.
Despite the encouraging performance achieved by GNN, less attention has been
paid to the privacy-preserving training and inference over distributed graph
data in the related literature. Due to the particularity of graph structure, it
is challenging to extend the existing private learning framework to GNN.
Motivated by the idea of split learning, we propose a \textbf{S}erver
\textbf{A}ided \textbf{P}rivacy-preserving \textbf{GNN} (SAPGNN) for the node
level task on horizontally partitioned cross-silo scenario. It offers a natural
extension of centralized GNN to isolated graph with max/min pooling
aggregation, while guaranteeing that all the private data involved in
computation still stays at local data holders. To further enhancing the data
privacy, a secure pooling aggregation mechanism is proposed. Theoretical and
experimental results show that the proposed model achieves the same accuracy as
the one learned over the combined data.
