Vanishing Gradients

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

Aug 14, 2023

Hugo chats with Katharine Jarmul, a Principal Data Scientist at Thoughtworks Germany, specializing in privacy and ethics in data workflows. They dive into the vital distinctions between data privacy and security, demystifying common misconceptions. Katharine highlights the impact of GDPR and CCPA, and explores advanced concepts like federated learning and differential privacy. They also tackle real-world issues like privacy attacks and the ethical responsibilities of data scientists, making a compelling case for prioritizing privacy in data practices.

01:23:19

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Episode guests

Katharine Jarmul

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Episode notes

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Quick takeaways

Data privacy must encompass cultural, legal, and technical dimensions, underscoring the need for multidisciplinary approaches in its implementation.

Effective data governance involves multiple stakeholders and ensures ethical data usage through comprehensive policies on retention and deletion.

Innovative techniques like differential privacy and federated learning are essential for safeguarding privacy in machine learning workflows and preventing data misuse.

Deep dives

Understanding Data Privacy and Security

Data privacy encompasses various dimensions such as cultural, legal, and technical aspects, highlighting the need for a multidisciplinary approach. Technical privacy focuses on how these social and legal definitions are translated into technical systems. Implementing privacy-enhancing technologies is crucial, yet challenges arise from integrating user interface design and consent mechanisms effectively. A significant aspect is ensuring that users' preferences regarding their privacy are captured in data systems, making privacy a collective responsibility rather than an individual concern.

Intro

4min

Navigating Data Privacy in the Digital Age

22min

Navigating Privacy Through Differential Technology

19min

Decrypting Data Science: Cryptography Unveiled

5min

Integrating Privacy into Data Science: A Hierarchical Approach

2min

Cultural Biases in AI Language Models

2min

Navigating Data Governance

10min

Navigating Data Deletion Challenges

18min

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!

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Vanishing Gradients

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

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Understanding Data Privacy and Security

The Role of Governance in Data Privacy

Technical Approaches to Privacy Preservation

Challenges of Implementing Privacy in Machine Learning

A Call to Action for Data Professionals

Remember Everything You Learn from Podcasts