主题：Graph Release with Assured Node Differential Privacy
主讲人：明尼苏达大学Tianxi Li副教授
主持人：统计与数据科学学院陈雪蓉教授
时间：2026年4月10日（周五）上午10：00-11：00
地点：线上报告，腾讯会议ID：824-884-520
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
Tianxi Li 是明尼苏达大学双子城分校统计学院的副教授，同时担任统计及其应用研究所（IRSA）所长，并担任数据科学研究生导师。他的研究主要集中在统计网络建模、机器学习以及高维统计领域。Tianxi Li 博士于2018年获得密歇根大学统计学博士学位。
内容提要：
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 talk, I will introduce 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.
