【online】 Balancing Inferential Integrity and Disclosure Risk via Model ......
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题目: Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation
报告人：Dr. Linglong Kong (Department of Mathematical and Statistical Sciences, University of Alberta, Canada)
报告方式：腾讯会议平台 ID：140 324 213
摘要: There is a growing expectation that data collected by government-funded studies should be openly available to ensure research reproducibility, which also increases concerns about data privacy. A strategy to protect individuals' identity is to release multiply imputed (MI) synthetic datasets with masked sensitivity values (Rubin, 1993). However, information loss or incorrectly specified imputation models can weaken or invalidate the inferences obtained from the MI-datasets. We propose a new masking framework with a data-augmentation (DA) component and a tuning mechanism that balances protecting identity disclosure against preserving data utility. Applying it to a restricted-use Canadian Scleroderma Research Group (CSRG) dataset, we found that this DA-MI strategy achieved a $0 \%$ identity disclosure risk and preserved all inferential conclusions. It yielded $95 \%$ confidence intervals (CIs) that had overlaps of $98.5 \%$ $(95.5 \%)$ on average with the CIs constructed using the full, unmasked CSRG dataset in a work-disability (interstitial lung disease) study. The CI-overlaps were lower for several other methods considered, ranging from $73.9 \%$ to $91.9 \%$ on average with the lowest value being $28.1 \%$; such low CI-overlaps further led to some incorrect inferential conclusions. These findings indicate that the DA-MI masking framework facilitates sharing of useful research data while protecting participants' identities.Joint work with Adrian E. Raftery, Russell J. Steele, and Naisyin Wang
报告人简介：Dr. Linglong Kong is an associate professor at the department of Mathematical and Statistical Sciences of the University of Alberta. He is a Canadian Research Chair in Statistical Learning. He has published more than 50 peer-reviewed manuscripts including top journals AOS, JASA and JRSSB, and top conferences ICML, ICDM, AAAI and IJCAI. Currently, Dr. Linglong Kong is serving as associate editors of Journal of the American Statistical Association, International Journal of Imaging Systems and Technology, Canadian Journal of Statistics, member of the Board of Directors of the Statistics Society of Canada and Western North American Region of The International Biometric Society, the ASA Statistical Imaging Session program chair-past and the ASA Statistical Computing Session program chair-elect. His research interests include statistical machine learning, high-dimensional data analysis, neuroimaging data analysis, robust statistics and quantile regression.