Noise-Aware Statistical Inference with Differentially Private Synthetic Data
Finnish Center for Artificial Intelligence FCAI via YouTube
Overview
Syllabus
Intro
Privacy-preserving ML @ FCAI
Introduction: Synthetic Data
Introduction: Differential Privacy
Introduction: Analysing Synthetic Data
Background: Differential Privacy
The Solution: Noise-Aware Multiple Imputation (NA+MI)
Rubin's Rules
The Bayesian Model - Variables
Results - Toy Example
Results - UCI Adult Dataset
Results - Marginal Accuracy on Adult
Limitations
Conclusion
Taught by
Finnish Center for Artificial Intelligence FCAI