GRAND: Graph Release with Assured Node Differential Privacy
This addresses privacy concerns for network data users, offering a novel solution for secure graph release.
The authors tackled the problem of releasing entire networks with node-level differential privacy while preserving structural properties, proposing GRAND as the first such mechanism and showing it asymptotically matches the original network distribution under latent space models.
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 propose GRAND (Graph Release with Assured Node Differential privacy), which is, to the best of our knowledge, the first network release mechanism that releases entire networks while ensuring node-level differential privacy and preserving structural properties. Under a broad class of latent space models, we show that the released network asymptotically follows the same distribution as the original network. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets.