A1239
Title: Releasing network data with node differential privacy
Authors: Tianxi Li - University of Minnesota (United States) [presenting]
Abstract: 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. The purpose is to present, to the best of 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, it is demonstrated that the released network asymptotically follows the same distribution as the original network and preserves global network moments. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets.
