Privacy-preserving machine learning

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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Detalles Bibliográficos
Otros Autores: Chang, J. Morris, author (author), Zhuang, Di , author, Samaraweera, G., author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Shelter Island : Manning Publications [2023]
Materias:
Machine learning.
Computer networks > Security measures.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009742721606719
Tabla de Contenidos:
  • 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
  • 4 Local differential privacy for machine learning
  • 4.1 What is local differential privacy?
  • 4.1.1 The concept of local differential privacy
  • 4.1.2 Randomized response for local differential privacy
  • 4.2 The mechanisms of local differential privacy
  • 4.2.1 Direct encoding
  • 4.2.2 Histogram encoding
  • 4.2.3 Unary encoding
  • Summary
  • 5 Advanced LDP mechanisms for machine learning
  • 5.1 A quick recap of local differential privacy
  • 5.2 Advanced LDP mechanisms
  • 5.2.1 The Laplace mechanism for LDP
  • 5.2.2 Duchi's mechanism for LDP
  • 5.2.3 The Piecewise mechanism for LDP
  • 5.3 A case study implementing LDP naive Bayes classification
  • 5.3.1 Using naive Bayes with ML classification
  • 5.3.2 Using LDP naive Bayes with discrete features
  • 5.3.3 Using LDP naive Bayes with continuous features.
  • 5.3.4 Evaluating the performance of different LDP protocols
  • Summary
  • 6 Privacy-preserving synthetic data generation
  • 6.1 Overview of synthetic data generation
  • 6.1.1 What is synthetic data? Why is it important?
  • 6.1.2 Application aspects of using synthetic data for privacy preservation
  • 6.1.3 Generating synthetic data
  • 6.2 Assuring privacy via data anonymization
  • 6.2.1 Private information sharing vs. privacy concerns
  • 6.2.2 Using k-anonymity against re-identification attacks
  • 6.2.3 Anonymization beyond k-anonymity
  • 6.3 DP for privacy-preserving synthetic data generation
  • 6.3.1 DP synthetic histogram representation generation
  • 6.3.2 DP synthetic tabular data generation
  • 6.3.3 DP synthetic multi-marginal data generation
  • 6.4 Case study on private synthetic data release via feature-level micro-aggregation
  • 6.4.1 Using hierarchical clustering and micro-aggregation
  • 6.4.2 Generating synthetic data
  • 6.4.3 Evaluating the performance of the generated synthetic data
  • Summary
  • Part 3 Building privacy-assured machine learning applications
  • 7 Privacy-preserving data mining techniques
  • 7.1 The importance of privacy preservation in data mining and management
  • 7.2 Privacy protection in data processing and mining
  • 7.2.1 What is data mining and how is it used?
  • 7.2.2 Consequences of privacy regulatory requirements
  • 7.3 Protecting privacy by modifying the input
  • 7.3.1 Applications and limitations
  • 7.4 Protecting privacy when publishing data
  • 7.4.1 Implementing data sanitization operations in Python
  • 7.4.2 k-anonymity
  • 7.4.3 Implementing k-anonymity in Python
  • Summary
  • 8 Privacy-preserving data management and operations
  • 8.1 A quick recap of privacy protection in data processing and mining
  • 8.2 Privacy protection beyond k-anonymity
  • 8.2.1 l-diversity
  • 8.2.2 t-closeness.
  • 8.2.3 Implementing privacy models with Python
  • 8.3 Protecting privacy by modifying the data mining output
  • 8.3.1 Association rule hiding
  • 8.3.2 Reducing the accuracy of data mining operations
  • 8.3.3 Inference control in statistical databases
  • 8.4 Privacy protection in data management systems
  • 8.4.1 Database security and privacy: Threats and vulnerabilities
  • 8.4.2 How likely is a modern database system to leak private information?
  • 8.4.3 Attacks on database systems
  • 8.4.4 Privacy-preserving techniques in statistical database systems
  • 8.4.5 What to consider when designing a customizable privacy-preserving database system
  • Summary
  • 9 Compressive privacy for machine learning
  • 9.1 Introducing compressive privacy
  • 9.2 The mechanisms of compressive privacy
  • 9.2.1 Principal component analysis (PCA)
  • 9.2.2 Other dimensionality reduction methods
  • 9.3 Using compressive privacy for ML applications
  • 9.3.1 Implementing compressive privacy
  • 9.3.2 The accuracy of the utility task
  • 9.3.3 The effect of ρ' in DCA for privacy and utility
  • 9.4 Case study: Privacy-preserving PCA and DCA on horizontally partitioned data
  • 9.4.1 Achieving privacy preservation on horizontally partitioned data
  • 9.4.2 Recapping dimensionality reduction approaches
  • 9.4.3 Using additive homomorphic encryption
  • 9.4.4 Overview of the proposed approach
  • 9.4.5 How privacy-preserving computation works
  • 9.4.6 Evaluating the efficiency and accuracy of the privacy-preserving PCA and DCA
  • Summary
  • 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
  • 10.1 The significance of a research data protection and sharing platform
  • 10.1.1 The motivation behind the DataHub platform
  • 10.1.2 DataHub's important features
  • 10.2 Understanding the research collaboration workspace
  • 10.2.1 The architectural design.
  • 10.2.2 Blending different trust models
  • 10.2.3 Configuring access control mechanisms
  • 10.3 Integrating privacy and security technologies into DataHub
  • 10.3.1 Data storage with a cloud-based secure NoSQL database
  • 10.3.2 Privacy-preserving data collection with local differential privacy
  • 10.3.3 Privacy-preserving machine learning
  • 10.3.4 Privacy-preserving query processing
  • 10.3.5 Using synthetic data generation in the DataHub platform
  • Summary
  • Appendix A. More details about differential privacy
  • A.1 The formal definition of differential privacy
  • A.2 Other differential privacy mechanisms
  • A.2.1 Geometric mechanism
  • A.2.2 Gaussian mechanism
  • A.2.3 Staircase mechanism
  • A.2.4 Vector mechanism
  • A.2.5 Wishart mechanism
  • A.3 Formal definitions of composition properties of DP
  • A.3.1 The formal definition of sequential composition DP
  • A.3.2 The formal definition of parallel composition DP
  • references
  • Appendix
  • index
  • inside back cover.

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