Galois Tech Talks

abstract:
In this talk, I’ll give an introduction to differential privacy with an emphasis on its relationship to machine learning, and its usefulness outside of privacy. Along the way, I’ll give a taste for the mathematical tools that can be used to achieve differential privacy. My thesis is that anyone who cares about data should care about the tools that the differential privacy literature offers.

bio:
Katrina Ligett is an assistant professor of computer science and economics at Caltech. Before joining Caltech in 2011, she did postdoctoral work at Cornell, and she received her PhD in computer science from Carnegie Mellon in 2009. Her primary research interests are in mathematical foundations for data privacy, and in game theory. She has received an NSF Career Award, a Microsoft Research Faculty Fellowship, a Google Faculty Research Award, and an Okawa Foundation Research Grant.

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Galois Tech Talks

Galois Video Plus

This channel contains video from the tech talks presented by galois.com

Galois has been holding weekly technical seminars since 2006 on topics from functional programming, formal methods, compiler and language design, to cryptography, and operating


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This channel contains video from the tech talks presented by galois.com

Galois has been holding weekly technical seminars since 2006 on topics from functional programming, formal methods, compiler and language design, to cryptography, and operating system construction, with talks by many figures from the programming language and formal methods communities. The talks are open and free.

Each week new tech talks are published at galois.com/blog/category/tech-talks/ and you can find out about upcoming talks on twitter.com/galoisinc

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