GraphPub: Generation of Differential Privacy Graph with High Availability

arXiv:2403.00030v1 Announce Type: cross
Abstract: In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks. However, when an upstream data owner publishes graph data, there are often many privacy concerns, because many real-world graph data contain sensitive information like person's friend list. Differential privacy (DP) is a common method to protect privacy, but due to the complex topological structure of graph data, applying DP on graphs often …

arxiv availability cs.ai cs.cr cs.lg cs.si data data owner datasets development differential privacy graph high high availability information networks neural networks privacy privacy concerns rapid real sensitive sensitive information upstream world