Private Graph Neural Network

Private graph neural networks (PGNNs) aim to leverage the power of graph neural networks for learning from graph-structured data while preserving the privacy of sensitive node and edge information. Current research focuses on developing differentially private algorithms, often employing techniques like noise addition to aggregated node embeddings, singular value perturbation of adjacency matrices, or importance-grained noise adaptation, to achieve a balance between privacy guarantees and model utility. These advancements are significant because they enable the application of powerful graph-based machine learning models to sensitive data in domains like social networks and healthcare, where privacy is paramount.