5 papers
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…
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…
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…
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…