Private Graph

Private graph research focuses on developing methods for analyzing graph data while preserving the privacy of individuals or entities represented by nodes and edges. Current efforts concentrate on creating privacy-preserving graph embedding algorithms, often employing techniques like local differential privacy and adversarial learning within frameworks such as graph neural networks (GNNs) and variational autoencoders (VAEs), to mitigate privacy vulnerabilities arising from graph representation learning. This field is crucial for enabling the analysis of sensitive network data in domains like social networks and healthcare, while addressing concerns about data breaches and unauthorized inference of private information.