美乐棋牌

报告题目：Distributed privacy-preserving group inference for high-dimensional generalized linear models

主讲人：韩东啸副教授（南开大学）

时间：2026年5月13日（周三）10:00 a.m.

形式：线上讲座

腾讯会议：989-696-431

主办单位：统计与数学学院

摘要：

We introduces a novel method for distributed, differentially private group inference in the high-dimensional generalized linear model. We consider a setting with an untrusted server and a group of trusted, data-holding clients. Each client first constructs a local non-private test statistic based on a weighted quadratic function of the regression sub-vector corresponding to the group, and the debiasing and re-weighting techniques. We then formulate our global differentially private test statistic by using the one-shot method, the Gaussian mechanism, and encryption and decryption procedures. Our proposed approach offers several key advantages: By operating under the untrusted server setting, our method resolves the critical weaknesses endemic to trust-based architectures, namely, systemic fragility and exposure to adversarial or governmental targeting. Moreover, the approach incorporates a bounded encryption procedure to ensure secure communication, eliminate the risk of the server actively leaking data, enable easy computation of the estimator’s sensitivity. and circumvent the computation of intractable sensitivities of complex statistics. Furthermore, our proposed method is capable of handling highly correlated covariates and preserving high power for identifying dense but weak signals. Unlike conventional methods, it also avoids the need to handle the Hessian and precision matrices. Simulation studies are carried out to examine the finite-sample behaviour of the proposed method. An application to an adult income dataset is provided.

主讲人简介：

韩东啸，南开大学统计与数据科学学院，副教授。2011年获得南开大学学士学位，2016年获得中国科学院大学博士学位，主要研究方向为生存分析、高维统计推断、机器学习理论研究。在统计学国际顶尖期刊“JASA”、机器学习顶级期刊“JMLR”、计量经济学顶级期刊“JOE”等上发表论文十余篇。主持国家自然科学基金面上项目1项、国家自然科学基金青年项目1项。担任中国现场统计研究会贝叶斯统计分会首届常务理事、全国工业统计学教学研究会数字经济与区块链协会常务理事、全国工业统计学教学研究会青年统计学家协会第二届理事会理事、中国现场统计研究会资源与环境统计分会理事。