@article {1021596,
	title = {Differential Privacy: A Primer for a Non-technical Audience},
	journal = {Vanderbilt Journal of Entertainment and Technology Law },
	volume = {21},
	number = {1},
	year = {2018},
	month = {2018},
	pages = {209-276},
	publisher = {a product of the "Bridging Privacy Definitions" working group, part of the Privacy Tools for Sharing Research Data project at Harvard University},
	address = {Cambridge, MA},
	abstract = {
	This document is a primer on differential privacy, which is a formal mathematical framework for guaranteeing privacy protection when analyzing or releasing statistical data. Recently emerging from the theoretical computer science literature, differential privacy is now in initial stages of implementation and use in various academic, industry, and government settings. Using intuitive illustrations and limited mathematical formalism, this document provides an introduction to differential privacy for non-technical practitioners, who are increasingly tasked with making decisions with respect to differential privacy as it grows more widespread in use. In particular, the examples in this document illustrate ways in which social scientists can conceptualize the guarantees provided by differential privacy with respect to the decisions they make when managing personal data about research subjects and informing them about the privacy protection they will be afforded.\&nbsp;



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},
	author = {Kobbi Nissim and Thomas Steinke and Alexandra Wood and Micah Altman and Aaron Bembenek and Mark Bun and Marco Gaboardi and O{\textquoteright}Brien, David and Salil Vadhan}
}
