Practical Data Privacy Katharine Jarmul

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Spis treści książki

• Foreword
• Preface
  • What Is Data Privacy?
  • Who Should Read This Book
    • Privacy Engineering
  • Why I Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • Acknowledgments
• 1. Data Governance and Simple Privacy Approaches
  • Data Governance: What Is It?
  • Identifying Sensitive Data
    • Identifying PII
  • Documenting Data for Use
    • Basic Data Documentation
      • Data Collection
      • Data Quality
      • Data Security
      • Data Privacy
      • Data Definitions
      • Descriptive Statistics
    • Finding and Documenting Unknown Data
    • Tracking Data Lineage
    • Data Version Control
  • Basic Privacy: Pseudonymization for Privacy by Design
  • Summary
• 2. Anonymization
  • What Is Anonymization?
  • Defining Differential Privacy
  • Understanding Epsilon: What Is Privacy Loss?
  • What Differential Privacy Guarantees, and What It Doesnt
  • Understanding Differential Privacy
    • Differential Privacy in Practice: Anonymizing the US Census
  • Differential Privacy with the Laplace Mechanism
    • Differential Privacy with Laplace: A Naive Attempt
    • Sensitivity and Error
    • Privacy Budgets and Composition
  • Exploring Other Mechanisms: Gaussian Noise for Differential Privacy
    • Comparing Laplace and Gaussian Noise
    • Real-World Differential Privacy: Debiasing Noisy Results
  • Sensitivity and Privacy Units
  • What About k-Anonymity?
  • Summary
• 3. Building Privacy into Data Pipelines
  • How to Build Privacy into Data Pipelines
    • Design Appropriate Privacy Measures
    • Meet Users Where They Are
    • Engineer Privacy In
    • Test and Verify
  • Engineering Privacy and Data Governance into Pipelines
    • An Example Data Sharing Workflow
    • Adding Provenance and Consent Information to Collection
  • Using Differential Privacy Libraries in Pipelines
  • Collecting Data Anonymously
    • Apples Differentially Private Data Collection
    • Why Chromes Original Differential Privacy Collection Died
  • Working with Data Engineering Team and Leadership
    • Share Responsibility
    • Create Workflows with Documentation and Privacy
    • Privacy as a Core Value Proposition
  • Summary
• 4. Privacy Attacks
  • Privacy Attacks: Analyzing Common Attack Vectors
    • Netflix Prize Attack
    • Linkage Attacks
    • Singling Out Attacks
    • Strava Heat Map Attack
    • Membership Inference Attack
    • Inferring Sensitive Attributes
    • Other Model Leakage Attacks: Memorization
    • Model-Stealing Attacks
    • Attacks Against Privacy Protocols
  • Data Security
    • Access Control
    • Data Loss Prevention
    • Extra Security Controls
    • Threat Modeling and Incident Response
  • Probabilistic Reasoning About Attacks
    • An Average Attacker
    • Measuring Risk, Assessing Threats
  • Data Security Mitigations
    • Applying Web Security Basics
    • Protecting Training Data and Models
    • Staying Informed: Learning About New Attacks
  • Summary
• 5. Privacy-Aware Machine Learning and Data Science
  • Using Privacy-Preserving Techniques in Machine Learning
    • Privacy-Preserving Techniques in a Typical Data Science or ML Workflow
      • Exploratory data analysis
      • Feature Engineering
    • Privacy-Preserving Machine Learning in the Wild
    • Differentially Private Stochastic Gradient Descent
  • Open Source Libraries for PPML
    • Engineering Differentially Private Features
    • Applying Simpler Methods
    • Documenting Your Machine Learning
    • Other Ways of Protecting Privacy in Machine Learning
  • Architecting Privacy in Data and Machine Learning Projects
    • Understanding Your Data Privacy Needs
    • Monitoring Privacy
  • Summary
• 6. Federated Learning and Data Science
  • Distributed Data
    • Why Use Distributed Data?
    • How Does Distributed Data Analysis Work?
    • Privacy-Charging Distributed Data with Differential Privacy
  • Federated Learning
    • Federated Learning: A Brief History
    • Why, When, and How to Use Federated Learning
  • Architecting Federated Systems
    • Example Deployment
    • Security Threats
    • Use Cases
    • Deploying Federated Libraries and Tools
  • Open Source Federated Libraries
    • Flower: Unified OSS for Federated Learning Libraries
  • A Federated Data Science Future Outlook
  • Summary
• 7. Encrypted Computation
  • What Is Encrypted Computation?
  • When to Use Encrypted Computation
    • Privacy Versus Secrecy
    • Threat Modeling
  • Types of Encrypted Computation
    • Secure Multiparty Computation
      • Secret Sharing
      • Security models in MPC
      • Factors to consider when using MPC
    • Homomorphic Encryption
      • Paillier encryption
      • Learning with errors
      • Factors to consider when using HE
      • Validating HE computations via zero-knowledge proofs
  • Real-World Encrypted Computation
    • Private Set Intersection
      • Diffie-Hellman for PSI
    • Private Join and Compute
    • Secure Aggregation
    • Encrypted Machine Learning
  • Getting Started with PSI and Moose
  • Imagining a World with Secure Data Sharing
  • Summary
• 8. Navigating the Legal Side of Privacy
  • GDPR: An Overview
    • Fundamental Data Rights Under GDPR
    • Data Controller Versus Data Processor
    • Applying Privacy-Enhancing Technologies for GDPR
    • GDPRs Data Protection Impact Assessment: Agile and Iterative Risk Assessments
    • Right to an Explanation: Interpretability and Privacy
  • California Consumer Privacy Act (CCPA)
    • Applying PETs for CCPA
  • Other Regulations: HIPAA, LGPD, PIPL, and More!
  • Internal Policies and Contracts
    • Reading Privacy Policies and Terms of Service
    • Reading Data Processing Agreements
    • Reading Policies, Guidelines, and Contracts
  • Working with Legal Professionals
    • Adhering to Contractual Agreements and Contract Law
    • Interpreting Data Protection Regulations
    • Asking for Help and Advice
    • Working Together on Shared Definitions and Ideas
    • Providing Technical Guidance
  • Data Governance 2.0
    • What Is Federated Governance?
    • Supporting a Culture of Experimentation
    • Documentation That Works, Platforms with PETs
  • Summary
• 9. Privacy and Practicality Considerations
  • Getting Practical: Managing Privacy and Security Risk
    • Evaluating and Managing Privacy Risk
    • Embracing Uncertainty While Planning for the Future
  • Practical Privacy Technology: Use-Case Analysis
    • Federated Marketing: Guiding Marketing Campaigns with Privacy Built In
      • Problem statement and desired outcome
      • Current solution
      • Privacy-first future solution
    • Public-Private Partnerships: Sharing Data for Public Health
      • Problem statement and desired outcome
      • Current solution
      • Privacy-first future solution
    • Anonymized Machine Learning: Looking for GDPR Compliance in Iterative Training Settings
      • Problem statement and desired outcome
      • Current solution
      • Privacy-first future solution
    • Business-to-Business Application: Hands-Off Data
      • Problem statement and desired outcome
      • Current solution
      • Privacy-first future solution
  • Step-by-Step: How to Integrate and Automate Privacy in ML
    • Iterative Discovery
    • Documenting Privacy Requirements
    • Evaluating and Combining Approaches
    • Shifting to Automation
    • Making Privacy Normal
  • Embracing the Future: Working with Research Libraries and Teams
    • Working with External Researchers
    • Investing in Internal Research
  • Summary
• 10. Frequently Asked Questions (and Their Answers!)
  • Encrypted Computation and Confidential Computing
    • Is Secure Computation Quantum-Safe?
    • Can I Use Enclaves to Solve Data Privacy or Data Secrecy Problems?
    • What If I Need to Protect the Privacy of the Client or User Who Sends the Database Query or Request?
    • Do Clean Rooms or Remote Data Analysis/Access Solve My Privacy Problem?
    • I Want to Provide Perfect Privacy or Perfect Secrecy. Is That Possible?
    • How Do I Determine That an Encrypted Computation Is Secure Enough?
    • If I Want to Use Encrypted Computation, How Do I Manage Key Rotation?
    • What Is Googles Privacy Sandbox? Does It Use Encrypted Computation?
  • Data Governance and Protection Mechanisms
    • Why Isnt k-Anonymity Enough?
      • But what about with t-closeness and l-diversity?
    • I Dont Think Differential Privacy Works for My Use Case. What Do I Do?
    • Can I Use Synthetic Data to Solve Privacy Problems?
    • How Should Data Be Shared Ethically or What Are Alternatives to Selling Data?
    • How Can I Find All the Private Information That I Need to Protect?
    • I Dropped the Personal Identifiers, so the Data Is Safe Now, Right?
    • How Do I Reason About Data I Released in the Past?
    • Im Working on a BI Dashboard or Visualization. How Do I Make It Privacy-Friendly?
    • Who Makes Privacy Engineering Decisions? How Do I Fit Privacy Engineering into My Organization?
    • What Skills or Background Do I Need to Become a Privacy Engineer?
    • Why Didnt You Mention (Insert Technology or Company Here)? How Do I Learn More? Help!
  • GDPR and Data Protection Regulations
    • Do I Really Need to Use Differential Privacy to Remove Data from GDPR/CPRA/LGPD/etc. Requirements?
    • I Heard That I Can Use Personal Data Under GDPR for Legitimate Interest. Is That Correct?
    • I Want to Comply with Schrems II and Transatlantic Data Flows. What Are Possible Solutions?
  • Personal Choices and Social Privacy
    • What Email Provider, Browser, and Application Should I Use if I Care About My Privacy?
    • My Friend Has an Automated Home or Phone Assistant. I Dont Want It Listening to Me. What Should I Do?
    • I Gave Up on Privacy a Long Time Ago. I Have Nothing to Hide. Why Should I Change?
    • Can I Just Sell My Own Data to Companies?
    • I Like Personalized Ads. Why Dont You?
    • Is (Fill in the Blank) Listening to Me? What Should I Do About It?
  • Summary
• 11. Go Forth and Engineer Privacy!
  • Surveillance Capitalism and Data Science
    • Gig Workers and Surveillance at Work
    • Surveillance for Security
    • Luxury Surveillance
  • Vast Data Collection and Society
    • Machine Learning as Data Laundering
    • Disinformation and Misinformation
  • Fighting Back
    • Researching, Documenting, Hacking, Learning
    • Collectivizing Data
    • Regulation Fining Back
    • Supporting Community Work
  • Privacy Champions
    • Your Privacy-Aware Multitool
    • Building Trustworthy Machine Learning Systems
    • Privacy by Design
    • Privacy and Power
  • Tschüss
• Index

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