Differential Privacy in Artificial Intelligence Book
The most comprehensive and up-to-date resource on differential privacy for AI.
Book: Differential Privacy in Artificial Intelligence is the most comprehensive and up-to-date resource on differential privacy for AI systems, covering foundations, deployment guidance, and real-world impact.
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Table of contents
Prelims
Part I: Foundation
- Chapter 1: Overview and Fundamental Techniques
- Chapter 2: Local Differential Privacy for Privacy-preserving Machine Learning
- Chapter 3: Composition of Differential Privacy and Privacy Amplification by Subsampling
- Chapter 4: Data Release and Synthetic Data
Part II: Privacy in Optimization and Learning
- Chapter 5: Privacy Risks in Machine Learning
- Chapter 6: Private Optimization
- Chapter 7: Private Deep Learning
- Chapter 8: Private Federated Learning
Part III: Application Areas
- Chapter 9: Differential Privacy and Medical Data Analysis
- Chapter 10: Differential Privacy in Energy Systems
- Chapter 11: Image and Video Data Analysis
Part IV: Tools and Testing
- Chapter 12: Programming Frameworks for Differential Privacy
- Chapter 13: Machine Learning Tools
- Chapter 14: Challenges and Solutions to Deploying Differential Privacy
- Chapter 15: Testing Private Models
Part V: Policy and Social Values
- Chapter 16: Differential Privacy, Public Policy, and the Law
- Chapter 17: Relationships between Differential Privacy and Algorithmic Fairness