1 citations · 1 across the 1 of their papers we have counts for
6 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…