Privacy-in-Statistics-and-Machine-Learning

This is a practical tutorial based on Adam Smith Privacy in Statistics and Machine Learning Course.

I will try to explain the math using examples and python code. This will help beginners to get better understanding! The original course includes video lectures and slides so I suggest you start there and then practice here. Each topic will have its own colab notebook. All notebooks will be available publicly in github. You can use the [github discussion] section to ask questions and hopfully getting answers.

Notes:

• I will use photos and resources from these lectures and other resources, all source links will be available.
• This is still work on progress. I will try to add more contents whenever I have more free time
• Contribution is welcome!
• If you have questions, comments, corrections, or feedback, please post in the dicussion forum.

Contents:

• Introduction
• Reconstruction Attacks
• Differential Privacy Fundamentals
• Exponential Mechanism and Report Noisy Max
• The Binary Tree Mechanism
• Approximate DP
• Advanced Composition
• Private Empirical Risk Minimization
• Private Gradient Descent
• Factorization Mechanisms
• The Projection Mechanism
• Online Learning and Multiplicative Weights
• Synthetic Data Generation and Online Learning
• Two-Player Zero-Sum Games
• Synthetic Data in Practice
• The Limits of Privacy
• Private Statistical Inference
• Privacy and Adaptive Data Analysis
• Differential Privacy and the Census