Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

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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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Bibliographic Details
Online Access: Full Text (via Skillsoft)
Main Author: Chang, J. Morris
Other Authors: Zhuang, Di, Samaraweera, G. Dumindu
Format: eBook
Language:English
Published: New York : Manning Publications Co. LLC, 2023.
Subjects:
Machine learning.
Computer networks > Security measures.
Computer networks > Security measures
Machine learning
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

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