22 citations · 82 across the 24 of their papers we have counts for
24 papers
Statisticians Training STEM Educators in Statistics Methods and Pedagogy: A Case Study of Instructor Training in Bayesian Methods
Mine Dogucu, Jingchen Hu, Amy H Herring
Educating the next generation of scientists in statistical methodology is an important task. Educating their instructors in statistical content knowledge and pedagogical knowledge…
Advancing Microdata Privacy Protection: A Review of Synthetic Data
Jingchen Hu, Claire McKay Bowen
Synthetic data generation is a powerful tool for privacy protection when considering public release of record-level data files. Initially proposed about three decades ago, it has g…
Introducing Variational Inference in Statistics and Data Science Curriculum
Vojtech Kejzlar, Jingchen Hu
Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both underg…
Privacy Protection for Youth Risk Behavior Using Bayesian Data Synthesis: A Case Study to the YRBS
Yixiao Cao, Jingchen Hu
The large number of publicly available survey datasets of wide variety, albeit useful, raise respondent-level privacy concerns. The synthetic data approach to data privacy and conf…
Mechanisms for Global Differential Privacy under Bayesian Data Synthesis
Jingchen Hu, Matthew R. Williams, Terrance D. Savitsky
This paper introduces a new method that embeds any Bayesian model used to generate synthetic data and converts it into a differentially private (DP) mechanism. We propose an altera…
Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings
Shijie Guo, Jingchen Hu
When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, sim…
The Current State of Undergraduate Bayesian Education and Recommendations for the Future
Mine Dogucu, Jingchen Hu
As a result of the increased emphasis on mis- and over-use of -values in scientific research and the rise in popularity of Bayesian statistics, Bayesian education is becoming mo…
Bayesian Estimation of Attribute Disclosure Risks in Synthetic Data with the R Package
Ryan Hornby, Jingchen Hu
Synthetic data is a promising approach to privacy protection in many contexts. A Bayesian synthesis model, also known as a synthesizer, simulates synthetic values of sensitive vari…
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy
Jingchen Hu, Joerg Drechsler, Hang J. Kim
Recent research in differential privacy demonstrated that (sub)sampling can amplify the level of protection. For example, for -differential privacy and simple random sampling wi…
Private Tabular Survey Data Products through Synthetic Microdata Generation
Jingchen Hu, Terrance D. Savitsky, Matthew R. Williams
We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record…
Two-Phase Data Synthesis for Income: An Application to the NHIS
Kevin Ros, Henrik Olsson, Jingchen Hu
We propose a two-phase synthesis process for synthesizing income, a sensitive variable which is usually highly-skewed and has a number of reported zeros. We consider two forms of a…
Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat
Jingchen Hu, Olanrewaju Akande, Quanli Wang
In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular at statistical agencies, the respondent-level data they collect from surv…
Identification Risks Evaluation of Partially Synthetic Data with the R Package
Ryan Hornby, Jingchen Hu
We extend a general approach to evaluating identification risk of synthesized variables in partially synthetic data. For multiple continuous synthesized variables, we introduce the…
Re-weighting of Vector-weighted Mechanisms for Utility Maximization under Differential Privacy
Terrance D. Savitsky, Jingchen Hu, Matthew R. Williams
We address practical implementation of a risk-weighted pseudo posterior synthesizer for microdata dissemination with a new re-weighting strategy that maximizes utility of released…
Online Statistics Teaching and Learning
Jim Albert, Mine Cetinkaya-Rundel, Jingchen Hu
For statistics courses at all levels, teaching and learning online poses challenges in different aspects. Particular online challenges include how to effectively and interactively…
Bayesian Computing in the Undergraduate Statistics Curriculum
Jim Albert, Jingchen Hu
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniq…
A Bayesian Statistics Course for Undergraduates: Bayesian Thinking, Computing, and Research
Jingchen Hu
We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students' Bayesian thinking with Bayesian me…
Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Terrance D. Savitsky, Matthew R. Williams, Jingchen Hu
We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic databases equipped with an probabilistic differential privacy (pDP) guarantee, where $δ…
Risk-Efficient Bayesian Data Synthesis for Privacy Protection
Jingchen Hu, Terrance D. Savitsky, Matthew R. Williams
Statistical agencies utilize models to synthesize respondent-level data for release to the public for privacy protection. In this work, we efficiently induce privacy protection int…
Bayesian Pseudo Posterior Synthesis for Data Privacy Protection
Jingchen Hu, Terrance D. Savitsky
Statistical agencies utilize models to synthesize respondent-level data for release to the general public as an alternative to the actual data records. A Bayesian model synthesizer…
Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys
Jingchen Hu, Terrance D. Savitsky
The release of synthetic data generated from a model estimated on the data helps statistical agencies disseminate respondent-level data with high utility and privacy protection. Mo…
Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data
Jingchen Hu
The synthetic data approach to data confidentiality has been actively researched on, and for the past decade or so, a good number of high quality work on developing innovative synt…
Synthesizing geocodes to facilitate access to detailed geographical information in large scale administrative data
Joerg Drechsler, Jingchen Hu
We investigate whether generating synthetic data can be a viable strategy for providing access to detailed geocoding information for external researchers, without compromising the…
Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data
Jingchen Hu, Jerome P. Reiter, Quanli Wang
We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data arise frequently in social science…