Safeguarding Privacy in AI: Techniques and Approaches

Data Privacy Protection Practices in the Development and Deployment of AI models.

Format

Online
Course

Expert

Rohan Sukumaran

Live session date

Feb 20, 2025

Live session time

12:30 - 3:00PM

Individual preparatory work

1 Hour

Price

$225 + Tax

About the module

In today's society, where personal data is a valuable currency, safeguarding privacy is paramount. This module will equip participants with knowledge of privacy-preserving technical solutions to integrate into AI design and deployment, ensuring regulatory compliance while fostering user trust. The importance of privacy in machine learning will be illustrated, alongside the challenges that may arise and the techniques to address them.
Additionally, participants will explore potential trade-offs, particularly at the intersection of privacy and fairness, to navigate the complexities of ethical AI development effectively.

Learning Outcomes

Understand the importance of privacy in AI and machine learning.
Apply privacy-preserving technical solutions in AI design and deployment.
Analyze challenges related to privacy in machine learning and develop strategies to address them.
Explore trade-offs between privacy and fairness in ethical AI development.

Who is this module for?

CTO, machine learning engineers, data scientists, AI researchers, and AI product developers.

Tailored for participants with a foundational understanding of AI concepts and basic knowledge of probability, linear algebra and machine learning.

Course Lessons

Rohan Sukumaran

Rohan Sukumaran is a graduate student at Mila, working with Prof. Golnoosh Farnadi on Responsible AI, focusing on fairness and privacy in machine learning. His research aims to develop efficient, reliable, and responsible AI systems. Previously, he served as Research Manager at the PathCheck Foundation, a spin-off from the MIT Media Lab, where he was advised by Prof. Ramesh Raskar. In that role, he contributed to privacy-preserving machine learning, adversarial representation learning, and graph neural networks with applications in computational health.

He also co-founded the Data Informatics Center for Epidemiology (DICE) alongside Prof. Ramesh Raskar (MIT), Prof. Manuel Morales (University of Montreal), and Prof. Sue Feldman (University of Alabama at Birmingham). Demonstrating his commitment to societal impact through technology, he hosted the Global Health Innovators Seminar, a platform for speakers who leverage AI and computer science to tackle critical challenges, particularly in healthcare.

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