22 citations · 82 across the 24 of their papers we have counts for
5 papers · 1 filter
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…