Explore the intersection of privacy and generative AI in this 45-minute Google TechTalk presented by Tim Dockhorn from the University of Waterloo. Gain insights into Differentially Private Diffusion Models (DPDMs), a novel approach that combines the power of diffusion models with differential privacy techniques. Learn how DPDMs address the challenge of limited data in privacy-sensitive domains by generating synthetic data while preserving privacy. Discover the optimal design space for diffusion models under differential privacy constraints and understand the concept of noise multiplicity, a tailored modification of differentially private stochastic gradient descent. Delve into recent advancements that leverage public pre-training to enhance DPDM performance, offering a comprehensive overview of this cutting-edge research in machine learning and data privacy.
Overview
Syllabus
Differentially Private Diffusion Models
Taught by
Google TechTalks
Related Courses
-
Differentially Private Diffusion Models Generate Useful Synthetic Images
-
Differentially Private Diffusion Probabilistic Models for Image Synthesis
-
Data Privacy and Anonymization in Python
-
Differentially Private Synthetic Data Generation via Foundation Model APIs
-
Generating Synthetic Tabular Data that is Differentially Private
-
Improving the Privacy-Utility Tradeoff in Differentially Private Machine Learning with Public Data
