Practical Synthetic Data Generation. Balancing Privacy and the Broad Availability of Data

Spis treści ebooka

• Preface
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introducing Synthetic Data Generation
  • Defining Synthetic Data
    • Synthesis from Real Data
    • Synthesis Without Real Data
    • Synthesis and Utility
  • The Benefits of Synthetic Data
    • Efficient Access to Data
    • Enabling Better Analytics
    • Synthetic Data as a Proxy
    • Learning to Trust Synthetic Data
  • Synthetic Data Case Studies
    • Manufacturing and Distribution
    • Healthcare
      • Data for cancer research
      • Evaluating innovative digital health technologies
    • Financial Services
      • Synthetic data benchmarks
      • Software testing
    • Transportation
      • Microsimulation models
      • Data synthesis for autonomous vehicles
  • Summary
• 2. Implementing Data Synthesis
  • When to Synthesize
  • Identifiability Spectrum
  • Trade-Offs in Selecting PETs to Enable Data Access
    • Decision Criteria
    • PETs Considered
    • Decision Framework
    • Examples of Applying the Decision Framework
  • Data Synthesis Projects
    • Data Synthesis Steps
    • Data Preparation
  • The Data Synthesis Pipeline
  • Synthesis Program Management
  • Summary
• 3. Getting Started: Distribution Fitting
  • Framing Data
  • How Data Is Distributed
  • Fitting Distributions to Real Data
  • Generating Synthetic Data from a Distribution
    • Measuring How Well Synthetic Data Fits a Distribution
    • The Overfitting Dilemma
    • A Little Light Weeding
  • Summary
• 4. Evaluating Synthetic Data Utility
  • Synthetic Data Utility Framework: Replication of Analysis
  • Synthetic Data Utility Framework: Utility Metrics
    • Comparing Univariate Distributions
    • Comparing Bivariate Statistics
    • Comparing Multivariate Prediction Models
    • Distinguishability
  • Summary
• 5. Methods for Synthesizing Data
  • Generating Synthetic Data from Theory
    • Sampling from a Multivariate Normal Distribution
    • Inducing Correlations with Specified Marginal Distributions
    • Copulas with Known Marginal Distributions
  • Generating Realistic Synthetic Data
    • Fitting Real Data to Known Distributions
    • Using Machine Learning to Fit the Distributions
  • Hybrid Synthetic Data
  • Machine Learning Methods
  • Deep Learning Methods
  • Synthesizing Sequences
  • Summary
• 6. Identity Disclosure in Synthetic Data
  • Types of Disclosure
    • Identity Disclosure
    • Learning Something New
    • Attribute Disclosure
    • Inferential Disclosure
    • Meaningful Identity Disclosure
    • Defining Information Gain
    • Bringing It All Together
    • Unique Matches
  • How Privacy Law Impacts the Creation and Use of Synthetic Data
    • Issues Under the GDPR
      • Is the use of the original (real) dataset to generate and/or evaluate a synthetic dataset restricted or regulated under the GDPR?
      • Is sharing the original dataset with a third-party service provider to generate the synthetic dataset restricted or regulated under the GDPR?
      • Does the GDPR regulate or otherwise affect (if at all) the resulting synthetic dataset?
    • Issues Under the CCPA
      • Is the use of the original (real) dataset to generate and/or evaluate a synthetic dataset restricted or regulated under the CCPA?
      • Is sharing the original dataset with a third-party service provider to generate the synthetic dataset restricted or regulated under the CCPA?
      • Does the CCPA regulate or otherwise affect (if at all) the resulting synthetic dataset?
    • Issues Under HIPAA
      • Is the use of the original (real) dataset to generate and/or evaluate a synthetic dataset restricted or regulated under HIPAA?
      • Is sharing the original dataset with a third-party service provider to generate the synthetic dataset restricted or regulated under HIPAA?
      • Does HIPAA regulate or otherwise affect (if at all) the resulting synthetic dataset?
    • Article 29 Working Party Opinion
      • Singling out
      • Linkability
      • Inference
      • Closing comments on the Article 29 opinion
  • Summary
• 7. Practical Data Synthesis
  • Managing Data Complexity
    • For Every Pre-Processing Step There Is a Post-Processing Step
    • Field Types
    • The Need for Rules
    • Not All Fields Have to Be Synthesized
    • Synthesizing Dates
    • Synthesizing Geography
    • Lookup Fields and Tables
    • Missing Data and Other Data Characteristics
    • Partial Synthesis
  • Organizing Data Synthesis
    • Computing Capacity
    • A Toolbox of Techniques
    • Synthesizing Cohorts Versus Full Datasets
    • Continuous Data Feeds
    • Privacy Assurance as Certification
    • Performing Validation Studies to Get Buy-In
    • Motivated Intruder Tests
    • Who Owns Synthetic Data?
  • Conclusions
• Index

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