  Graphs are widely used to model the complex relationships among entities. As
a powerful tool for graph analytics, graph neural networks (GNNs) have recently
gained wide attention due to its end-to-end processing capabilities. With the
proliferation of cloud computing, it is increasingly popular to deploy the
services of complex and resource-intensive model training and inference in the
cloud due to its prominent benefits. However, GNN training and inference
services, if deployed in the cloud, will raise critical privacy concerns about
the information-rich and proprietary graph data (and the resulting model).
While there has been some work on secure neural network training and inference,
they all focus on convolutional neural networks handling images and text rather
than complex graph data with rich structural information. In this paper, we
design, implement, and evaluate SecGNN, the first system supporting
privacy-preserving GNN training and inference services in the cloud. SecGNN is
built from a synergy of insights on lightweight cryptography and machine
learning techniques. We deeply examine the procedure of GNN training and
inference, and devise a series of corresponding secure customized protocols to
support the holistic computation. Extensive experiments demonstrate that SecGNN
achieves comparable plaintext training and inference accuracy, with promising
performance.
