WDP-GAN: Weighted Graph Generation With GAN Under Differential Privacy

Abstract

Many real-world networks can be represented as weighted graphs, where weights represent the closeness or importance of relationships between node pairs. Sharing these graphs is beneficial for many applications while potentially leading to privacy breaches. Variants of deep learning approaches have been developed for synthetic graph publishing, but privacy-preserving graph (especially weighted graph) publishing has not been fully addressed. To bridge this gap, we propose WDP-GAN, a generative adversarial network (GAN) based privacy-preserving weighted graph generation approach, which can generate unlimited synthetic graphs of a given weighted graph while ensuring individual privacy. To do this, we devise a new node sequence sampling method to generate the training set while preserving both the edge weight and topological structure of the original graph. Moreover, we apply the bi-directional long-short term memory (Bi-LSTM) network to capture the interdependence of node pairs. WDP-GAN then approximates the edge weight information using the frequencies of edges produced by the generator. Furthermore, we propose an adaptive gradient perturbation algorithm to improve the speed and stability of the training process while ensuring individual privacy. Theoretical analysis and experiments on real-world network datasets show that WDP-GAN can generate graphs that effectively preserve structural utility while satisfying differential privacy.