Privacy Tech in AI Model Building

Explore the intersection of privacy and artificial intelligence in this 47-minute webinar sponsored by doc.ai. Delve into the challenges of utilizing rich datasets while safeguarding individual and community privacy, especially in light of accelerated AI adoption across various sectors during the pandemic. Learn about key concepts such as overfitting, federated learning, differential privacy, secure multiparty computation, and trusted execution environments. Gain insights into privacy-preserving techniques in AI model building, including zero trust architecture and homomorphic encryption. Understand the implications of GDPR on AI development and discover strategies to balance data utility with privacy protection. Engage with a comprehensive overview of privacy tech in AI, addressing critical issues in an era of rapid technological advancement.

Introduction
Problem Statement
Overfitting
Reducing Overfitting
Sensitive Access
Federated Learning
Differential Privacy
Secure Multiparty Compute
Trusted Execution Environment
Zero Trust
Recap
Questions
GDPR
Homomorphic Encryption
insecure multiparty compute