| Abstract |
Selection with privacy protection is increasingly important in high-dimensional data, particularly where data retains important yet sensitive information. In this paper, we propose a Safe Differentially Private model-free variable screening framework (SDPscreen) and further provide a private data-adaptive threshold with controlled false discovery rate (FDR) for high-dimensional sensitive data. Our private strategy is constructed upon the (boosted) Chatterjee's rank coefficient by incorporating a oneshot peeling algorithm. Our method is fully non-parametric, enabling the detection of non-linear and non-monotone associations without imposing the bounded conditions for privacy protection. We establish both algorithmic privacy guarantees and screening properties, and the private data-adaptive selection demonstrates the capability for FDR control under mild theoretical conditions. Extensive numerical experiments and a real-world application confirm that our methods outperform state-of-the-art approaches in terms of both screening accuracy and privacy efficiency. |
| Speaker Bio |
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 150 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.
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