Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. (arXiv:2309.08569v1 [cs.LG])

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 …

applications data differential privacy framework local low modeling networks neural networks node privacy protection studies utility work