主 题：Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference

主讲人：加拿大阿尔伯塔大学Linglong Kong教授

主持人：统计与数据科学学院陈雪蓉教授

时间：2025年8月4日（周一）上午10-11点

地点：线上会议，腾讯会议808-631-824

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：

Dr. Linglong Kong is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, holding a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a Fellow of the American Statistical Association (ASA) and the Alberta Machine Intelligence Institute (Amii), with over 120 peer-reviewed publications in leading journals and conferences such as AOS, JASA, JRSSB, NeurIPS, ICML, and ICLR. Dr. Kong received the 2025 CRM-SSC Prize for outstanding research in Canada. He serves as Associate Editor for several top journals, including JASA and AOAS, and has held leadership roles within the ASA and the Statistical Society of Canada. Dr. Kong’s research interests include high-dimensional and neuroimaging data analysis, statistical machine learning, robust statistics, quantile regression, trustworthy machine learning, and artificial intelligence for smart health.

Linglong Kong，加拿大阿尔伯塔大学数学与统计科学系教授，担任加拿大统计学习领域的Canada Research Chair 以及加拿大CIFAR人工智能讲席教授（Canada CIFAR AI Chair）。他是美国统计协会（ASA）和阿尔伯塔机器智能研究所（Amii）的Fellow，在 AOS, JASA, JRSSB, NeurIPS, ICML, and ICLR等顶级期刊和会议上发表了 120 多篇论文。Kong教授因其在加拿大的杰出研究成果获得2025年度CRM-SSC Prize（由加拿大数学研究中心与加拿大统计学会联合颁发）。他同时担任多个顶级统计期刊的Associate Editor，包括 JASA 和  AOAS，并在美国统计学会（ASA)与加拿大统计学会(Statistical Society of Canada)中担任多个重要职务。他的研究兴趣包括高维数据分析、脑成像数据分析、统计机器学习、稳健统计、分位数回归、可信机器学习，以及智慧医疗中的人工智能应用等方向。

内容提要：

Randomized response (RR) mechanisms constitute a fundamental and effective technique for ensuring label differential privacy (LabelDP). However, existing RR methods primarily focus on the response labels while overlooking the influence of covariates and often do not fully address optimality. To address these challenges, this paper explores optimal LabelDP procedures using RR mechanisms, focusing on achieving optimal estimation and inference in binary response models. We first analyze the asymptotic behaviors of RR binary response models and then optimize the procedure by maximizing the trace of the Fisher Information Matrix within the $\varepsilon$ - and $(\varepsilon, \delta)$-LabelDP constraints. Our theoretical results indicate that the proposed methods achieve optimal LabelDP guarantees while maintaining statistical accuracy in binary response models under mild conditions. Furthermore, we develop private confidence intervals with nominal coverage for statistical inference. Extensive simulation studies and real-world applications confirm that our methods outperform existing approaches in terms of precise estimation, privacy protection, and reliable inference.