09-15, 13:40–14:10 (Europe/Amsterdam), Bar
If you are curious about the field of cryptography and what it has to offer data science and machine learning, this talk is for you! We'll dive into the field of encrypted computation, where decryption isn't needed in order to perform calculations, transformations and operations on the data. You'll learn some of the core mathematical theory behind why and how this works, as well as the differences between approaches like homomorphic encryption and secure multi-party computation. At the end, you'll get some pointers and open-source library hints on where to go next and how to start using encrypted computation for problems you are solving the hard way (or not solving at all).
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No previous knowledge expected
Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy.