contents

 

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