Privacy-Preserving Decentralized Data Science with Andrew Trask

EPISODE 241

March 21, 2019

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Banner Image: Andrew Trask - Podcast Interview

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About this Episode

Today we're joined by Andrew Trask, PhD student at the University of Oxford and Leader of the OpenMined Project. OpenMined is an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. Andrew and I caught up back at NeurIPS to dig into why OpenMined is important and explore some of the basic research and technologies supporting Private, Decentralized Data Science. We touch on ideas such as Differential Privacy, and Secure Multi-Party Computation, and how these ideas come into play in, for example, federated learning.

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Thanks to our sponsor PegaSystems

Thanks to our friends at Pega for their support of the podcast and their sponsorship of today's show. Pega is a low-code platform for AI-powered decisioning and workflow automation. Its scalable architecture helps the world's leading organizations work smarter, unify experiences, and adapt instantly - so they're always ready for what's next. Check out the latest from Pega at their annual conference PegaWorld inspire, which will focus on how to address constantly shifting perspectives in an ever-evolving world, including guidance, strategies, and powerful tools to achieve resiliency in the face of rapid change. The event will be held virtually on May 24th for the Americas and Europe. And again, on May 25th for Asia Pacific. Agenda and registration details can be found at pegaworld.com.