Episode 19: Privacy and Security in Data Science and Machine Learning

Vanishing Gradients

Hugo speaks with Katharine Jarmul about privacy and security in data science and machine learning. Katharine is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics, and security for data science workflows. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy, and security.

In this episode, Hugo and Katharine talk about

• What data privacy and security are, what they aren’t and the differences between them (hopefully dispelling common misconceptions along the way!);
• Why you should care about them (hint: the answers will involve regulatory, ethical, risk, and organizational concerns);
• Data governance, anonymization techniques, and privacy in data pipelines;
• Privacy attacks!
• The state of the art in privacy-aware machine learning and data science, including federated learning;
• What you need to know about the current state of regulation, including GDPR and CCPA…

And much more, all the while grounding our conversation in real-world examples from data science, machine learning, business, and life!

You can also sign up for our next livestreamed podcast recording here!

LINKS

• Win a copy of Practical Data Privacy, Katharine's new book!
• Katharine on twitter
• Vanishing Gradients on YouTube
• Probably Private, a newsletter for privacy and data science enthusiasts
• Probably Private on YouTube