The Importance of Privacy in Machine Learning

A changing regulatory environment has made it more important than ever for organizations to embed privacy in their data infrastructure. Doing so, however, can be complicated — that means data scientists have an vital role to play in ensuring privacy is a key concern from both a technical and commercial perspective. 

Thoughtworker and data scientist Katharine Jarmul is eager to help fellow data scientists master privacy principles and techniques. Her new book, Practical Data Privacy, covers everything from the fundamentals of governance and anonymization through to advanced approaches to data privacy like federated learning and encrypted computation.

In this episode of the Technology Podcast, Katharine joins hosts Rebecca Parsons and Birgitta Böckeler to discuss the book and explain why data scientists need to be on the frontline in the fight for privacy. 

Find Practical Data Privacy on Amazon: https://www.amazon.com/Practical-Data-Privacy-Enhancing-Security/dp/1098129466