Privacy-Preserving Machine Learning
Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthe...
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Format: | eBook |
Language: | English |
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New York :
Manning Publications Co. LLC,
2023.
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Table of Contents:
- Intro
- inside front cover
- Privacy-Preserving Machine Learning
- Copyright
- contents
- front matter
- preface
- acknowledgments
- about this book
- Who should read this book
- How this book is organized: A road map
- About the code
- liveBook discussion forum
- 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
- 1.2.1 Use of private data on the fly
- 1.2.2 How data is processed inside ML algorithms
- 1.2.3 Why privacy protection in ML is important
- 1.2.4 Regulatory requirements and the utility vs. privacy tradeoff
- 1.3 Threats and attacks for ML systems
- 1.3.1 The problem of private data in the clear
- 1.3.2 Reconstruction attacks
- 1.3.3 Model inversion attacks
- 1.3.4 Membership inference attacks
- 1.3.5 De-anonymization or re-identification attacks
- 1.3.6 Challenges of privacy protection in big data analytics
- 1.4 Securing privacy while learning from data: Privacy-preserving machine learning
- 1.4.1 Use of differential privacy
- 1.4.2 Local differential privacy
- 1.4.3 Privacy-preserving synthetic data generation
- 1.4.4 Privacy-preserving data mining techniques
- 1.4.5 Compressive privacy
- 1.5 How is this book structured?
- Summary
- 2 Differential privacy for machine learning
- 2.1 What is differential privacy?
- 2.1.1 The concept of differential privacy
- 2.1.2 How differential privacy works
- 2.2 Mechanisms of differential privacy
- 2.2.1 Binary mechanism (randomized response)
- 2.2.2 Laplace mechanism
- 2.2.3 Exponential mechanism
- 2.3 Properties of differential privacy
- 2.3.1 Postprocessing property of differential privacy
- 2.3.2 Group privacy property of differential privacy
- 2.3.3 Composition properties of differential privacy
- Summary
- 3 Advanced concepts of differential privacy for machine learning
- 3.1 Applying differential privacy in machine learning
- 3.1.1 Input perturbation
- 3.1.2 Algorithm perturbation
- 3.1.3 Output perturbation
- 3.1.4 Objective perturbation
- 3.2 Differentially private supervised learning algorithms
- 3.2.1 Differentially private naive Bayes classification
- 3.2.2 Differentially private logistic regression
- 3.2.3 Differentially private linear regression
- 3.3 Differentially private unsupervised learning algorithms
- 3.3.1 Differentially private k-means clustering
- 3.4 Case study: Differentially private principal component analysis
- 3.4.1 The privacy of PCA over horizontally partitioned data
- 3.4.2 Designing differentially private PCA over horizontally partitioned data
- 3.4.3 Experimentally evaluating the performance of the protocol
- Summary
- Part 2 Local differential privacy and synthetic data generation