Network Generation with Differential Privacy

Understand

We consider the problem of generating private synthetic versions of real-world graphs containing private information while maintaining the utility of generated graphs.

• Differential privacy is a gold standard for data privacy, and the introduction of the differentially private stochastic gradient descent (DP-SGD) algorithm has facilitated the training of private neural models in a number of domains.
• Recent advances in graph generation via deep generative networks have produced several high performing models.
• We evaluate and compare state-of-the-art models including adjacency matrix based models and edge based models, and show a practical implementation that favours the edge-list approach utilizing the Gaussian noise mechanism when evaluated on commonly used graph datasets.