Episode from the podcastThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132

Released Monday, 30th April 2018
 1 person rated this episode
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132.
The discussion of non privacy-related benefits of differential privacy, as a tool to create synthetic datasets and increase model robustness, was interesting.
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