front matter
preface
acknowledgments
about this book
about the authors
about the cover illustration
Part 1 Basics of privacy-preserving machine learning with differential privacy
1 Privacy considerations in machine learning
1.1 Privacy complications in the AI era
1.2 The threat of learning beyond the intended purpose
Use of private data on the fly
How data is processed inside ML algorithms
Why privacy protection in ML is important
Regulatory requirements and the utility vs. privacy tradeoff
1.3 Threats and attacks for ML systems
The problem of private data in the clear
Reconstruction attacks
Model inversion attacks
Membership inference attacks
De-anonymization or re-identification attacks
Challenges of privacy protection in big data analytics
Use of differential privacy