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  • 1.  SLDS June webinar

    Posted 06-12-2024 22:40

    Dear Colleagues, 

    The SLDS June webinar features Dr. Bei Jiang from University of Alberta.  Dr. Jiang is an expert on analyzing privacy-preserving and fair synthetic data.  In this webinar, she will discuss about differential privacy.  Hope to see you there!  

    Title:                        Online Local Differential Private Quantile Inference

    Speakers:               Dr. Bei Jiang, Department of Mathematical and Statistical Sciences, University of Alberta

    Date and Time:      June 21, 2024, 1:00 to 2:30 pm Eastern Time

    Registration Link:   ASA SLDS Webinar Registration Link [] 

    Abstract:                Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotically normal estimation with valid inference, resulting in tight confidence intervals without the need for nuisance parameters to be estimated. Our proposed method can be conducted fully online, leading to high computational efficiency and minimal storage requirements with O(1) space. We also proved an optimality result by an elegant application of one central limit theorem of Gaussian Differential Privacy (GDP) when targeting the frequently encountered median estimation problem. With mathematical proof and extensive numerical testing, we demonstrate the validity of our algorithm both theoretically and experimentally.


    Presenter:               Dr. Bei Jiang is an Associate Professor at the Department of Mathematical and Statistical Sciences of the University of Alberta, a Fellow and a Canada CIFAR AI chair affiliated with the Alberta Machine Intelligence Institute. She received her PhD in Biostatistics in 2014 from University of Michigan. Prior to joining the University of Alberta in 2015 as an Assistant Professor, she was a postdoctoral researcher at the Department of Biostatistics at the Columbia University from 2014 to 2015. Her main research interests focus on Bayesian Hierarchical Modeling, Joint Modeling for Multimodal Health Data, Privacy-preserving and Fair Synthetic Data, Statistical Inference under Formal Privacy Guarantee and Fairness Constraint, and Federated Statistical Inference. She has also worked closely with collaborators in women's health, mental health, neurology, and industry partners to apply cutting-edge statistical learning methods to real-world applications.

    Zhihua Su, PhD
    Associate Professor
    Department of Statistics
    University of Florida