2 citations · 2 across the 6 of their papers we have counts for
6 papers
The Five Safes as a Privacy Context
James Bailie, Ruobin Gong
The Five Safes is a framework used by national statistical offices (NSO) for assessing and managing the disclosure risk of data sharing. It can be understood as a specialization of…
Differential Privacy Meets Invariant Statistics: Some Conundrums in Quantifying Trade-Offs
James Bailie, Ruobin Gong, Xiao-Li Meng
This work was inspired by the question of whether data swapping, a popular form of statistical disclosure control used to protect many data products including three recent US Decen…
A Refreshment Stirred, Not Shaken: Invariant-Preserving Deployments of Differential Privacy for the U.S. Decennial Census
James Bailie, Ruobin Gong, Xiao-Li Meng
Protecting an individual's privacy when releasing their data is inherently an exercise in relativity, regardless of how privacy is qualified or quantified. This is because we can o…
dapper: Data Augmentation for Private Posterior Estimation in R
Kevin Eng, Jordan A. Awan, Nianqiao Phyllis Ju +2
This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of param…
Differentially Private Range Queries with Correlated Input Perturbation
Prathamesh Dharangutte, Jie Gao, Ruobin Gong +1
This work proposes a class of differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achi…
General Inferential Limits Under Differential and Pufferfish Privacy
James Bailie, Ruobin Gong
Differential privacy (DP) is a class of mathematical standards for assessing the privacy provided by a data-release mechanism. This work concerns two important flavors of DP that a…