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Graphs are widely used to model the complex relationships among entities. Asa powerful tool for graph analytics, graph neural networks (GNNs) have recentlygained wide attention due to its end-to-end processing capabilities. With theproliferation of cloud computing, it is increasingly popular to deploy theservices of complex and resource-intensive model training and inference in thecloud due to its prominent benefits. However, GNN training and inferenceservices, if deployed in the cloud, will raise critical privacy concerns aboutthe 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 ratherthan complex graph data with rich structural information. In this paper, wedesign, implement, and evaluate SecGNN, the first system supportingprivacy-preserving GNN training and inference services in the cloud. SecGNN isbuilt from a synergy of insights on lightweight cryptography and machinelearning techniques. We deeply examine the procedure of GNN training andinference, and devise a series of corresponding secure customized protocols tosupport the holistic computation. Extensive experiments demonstrate that SecGNNachieves comparable plaintext training and inference accuracy, with promisingperformance.

SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service