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