Distribution-invariant Node Differential Privacy for Network Data

Music City Center 

Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data—particularly at the node level—remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based approaches, which restrict output to pre-specified network statistics, or fail to preserve key structural properties of the network. In this work, we present what is, to the best of our knowledge, the first mechanism capable of releasing an entire network structure while satisfying node-level differential privacy. Within the broad class of latent space models, we demonstrate that the released network asymptotically follows the same distribution as the original network and preserves global network moments. Additionally, our method supports individualized privacy budgets for each node, maintaining linkage between the released network and the original network under the privacy constraints. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets.