Node Level Privacy

Node-level privacy in graph neural networks (GNNs) focuses on protecting sensitive information associated with individual nodes within a graph during machine learning. Current research emphasizes developing differentially private GNNs, employing techniques like specialized node sampling, noise addition (e.g., using Laplace or Gaussian mechanisms), and novel graph convolution methods to achieve privacy guarantees while maintaining model accuracy. This area is crucial for enabling the use of GNNs in sensitive applications like social networks and healthcare, where protecting individual privacy is paramount, and advancements are driving the development of more robust and efficient privacy-preserving machine learning algorithms.