  Graph Neural Networks (GNNs) have demonstrated superior performance in
learning node representations for various graph inference tasks. However,
learning over graph data can raise privacy concerns when nodes represent people
or human-related variables that involve sensitive or personal information.
While numerous techniques have been proposed for privacy-preserving deep
learning over non-relational data, there is less work addressing the privacy
issues pertained to applying deep learning algorithms on graphs. In this paper,
we study the problem of node data privacy, where graph nodes have potentially
sensitive data that is kept private, but they could be beneficial for a central
server for training a GNN over the graph. To address this problem, we develop a
privacy-preserving, architecture-agnostic GNN learning algorithm with formal
privacy guarantees based on Local Differential Privacy (LDP). Specifically, we
propose an LDP encoder and an unbiased rectifier, by which the server can
communicate with the graph nodes to privately collect their data and
approximate the GNN's first layer. To further reduce the effect of the injected
noise, we propose to prepend a simple graph convolution layer, called KProp,
which is based on the multi-hop aggregation of the nodes' features acting as a
denoising mechanism. Finally, we propose a robust training framework, in which
we benefit from KProp's denoising capability to increase the accuracy of
inference in the presence of noisy labels. Extensive experiments conducted over
real-world datasets demonstrate that our method can maintain a satisfying level
of accuracy with low privacy loss.
