Differential privacy provides a mathematical definition of what privacy is in the context of user data. In lay terms, a data set is said to be differentially private if the existence or lack of existence of a particular piece of data doesn't impact the end result. Differential privacy protects an individual's information essentially as if her information were not used in the analysis at all.
This is a promising area of research and one of the future privacy-enhancing technologies that many people in the privacy community are excited about. However, it's not just theoretical, differential privacy is already being used by large technology companies like Google and Apple as well as in US Census result reporting.
Dr. Yun Lu of the University of Victoria specializes in differential privacy and she joins the show to explain differential privacy, why it's such a promising and compelling framework, and share some of her research on applying differential privacy in voting and election result reporting.
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