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, such as image, audio, video, and text, there
is less work addressing the privacy issues pertained to applying deep learning
algorithms on graphs. As a result and for the first time, 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, using 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 noisy
labels. Extensive experiments conducted over real-world datasets demonstrate
that our method can maintain a satisfying level of accuracy with low privacy
loss. Our implementation is available online.

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