Law Commons^™

Differential Privacy: A Primer For A Non-Technical Audience, Alexandra Wood, Micah Altman, Aaron Bembenek, Mark Bun, Marco Gaboardi, James Honaker, Kobbi Nissim, David R. O'Brien, Thomas Steinke, Salil Vadhan

Vanderbilt Journal of Entertainment & Technology Law

Differential privacy is a formal mathematical framework for quantifying and managing privacy risks. It provides provable privacy protection against a wide range of potential attacks, including those currently unforeseen. Differential privacy is primarily studied in the context of the collection, analysis, and release of aggregate statistics. These range from simple statistical estimations, such as averages, to machine learning. Tools for differentially private analysis are now in early stages of implementation and use across a variety of academic,industry, and government settings. Interest in the concept is growing among potential users of the tools, as well as within legal and policy communities, …