2 citations · 3 across the 7 of their papers we have counts for
7 papers
Big data, differential privacy, and national statistical organisations
James Bailie
Differential privacy (DP) has emerged in the computer science literature as a measure of the impact on an individual's privacy resulting from the publication of a statistical outpu…
Persuasive Privacy
Joshua J Bon, James Bailie, Judith Rousseau +1
We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that a…
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…
Property Elicitation on Imprecise Probabilities
James Bailie, Rabanus Derr
Property elicitation studies which attributes of a probability distribution can be determined by minimizing a risk. We investigate a generalization of property elicitation to impre…
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…
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…