报告主题：POMT: A  Private Online Multiple Testing Framework with Adaptive Budgets

时间：2026年9月24日10:00-11:00

地点：1-218

报告人：许王莉

报告内容简介：

Online multiple testing is increasingly prevalent in biomedical research and information technology, where protection of sensitive personal information is essential. To date, the only work on privacy-preserving online multiple testing is PAPRIKA \citep{zhang2021paprika}, which leverages log-transformed $p$-values, Laplace noise, and the sparse vector technique (SVT). While providing rigorous privacy and false discovery rate (FDR) guarantees, PAPRIKA distorts the super-uniformity of null perturbed $p$-values and introduces complex dependence among rejection thresholds, leading to conservative rejection rules at the cost of reduced power. We address these limitations by proposing a general Private Online Multiple Testing (POMT) framework. Our approach develops a powerful $p$-value transformation that preserves super-uniformity while achieving privacy guarantee,

and introduces a unified budget calibration rule that adaptively mitigates the additional Type-I error due to imposed privacy mechanisms. Theoretical analyses demonstrate that POMT guarantees finite-sample control of Type-I errors, including both the family-wise error rate (FWER) and FDR. We also establish upper and lower bounds for the power of POMT and demonstrate its power advantage over PAPRIKA. Extensive simulations and real data studies validate our theoretical findings and show that POMT provides strong privacy protection with only a modest loss of power compared to non-private baselines.

主讲人简介：

许王莉，中国人民大学吴玉章讲席教授，博士生导师。先后主持5项国家自然科学基金，北京市自然科学基金重点研究专题，教育部人文社会科学重点研究基地重大项目和教育部人文社科基金等多项科研课题。在顶尖期刊JASA, JRSSB, Biometrika, TPAMI等发表百余篇论文。先后入选“新世纪优秀人才计划”和“北京市科技新星计划”，先后获得中国第十二届北京市统计科研优秀成果奖一等奖(2014)，第一届统计科学技术进步二等奖（2021）。