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.

我们提出了一个基于图神经网络的学习框架，该框架可以在用户层面提供节点隐私，并且在损失效用性方面表现较低。我们使用分布式隐私保护的概念，并在节点水平对特征和标签数据应用随机化机制，在数据被中央服务器收集用于模型训练之前进行扰动。通过随机化数据的统计分析中的频率估计，我们开发了重构方法来逼近扰动数据的特征和标签。我们还形成了利用图聚类的频率估计来在子图层面监督训练过程的学习框架。在真实数据集和半合成数据集上进行了大量实验，证明了我们提出模型的有效性。