Local Differential Privacy in Graph Neural Networks: a Reconstruction
  Approach
Karuna Bhaila Wen Huang Yongkai Wu Xintao Wu
Graph Neural Networks have achieved tremendous success in modeling complex
graph data in a variety of applications. However, there are limited studies
investigating privacy protection in GNNs. In this work, we propose a learning
framework that can provide node privacy at the user level, while incurring low
utility loss. We focus on a decentralized notion of Differential Privacy,
namely Local Differential Privacy, and apply randomization mechanisms to
perturb both feature and label data at the node level before the data is
collected by a central server for model training. Specifically, we investigate
the application of randomization mechanisms in high-dimensional feature
settings and propose an LDP protocol with strict privacy guarantees. Based on
frequency estimation in statistical analysis of randomized data, we develop
reconstruction methods to approximate features and labels from perturbed data.
We also formulate this learning framework to utilize frequency estimates of
graph clusters to supervise the training procedure at a sub-graph level.
Extensive experiments on real-world and semi-synthetic datasets demonstrate the
validity of our proposed model.