Sept. 18, 2023, 1:10 a.m. | Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu

cs.CR updates on

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

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