5 papers
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…
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. This paper makes two points: Firstly, the F…
A Refreshment Stirred, Not Shaken (III): Can Swapping Be Differentially Private?
James Bailie, Ruobin Gong, Xiao-Li Meng
The quest for a precise and contextually grounded answer to the question in the present paper's title resulted in this stirred-not-shaken triptych, a phrase that reflects our desir…
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…