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Book Description

How can you use data in a way that protects individual privacy, but still ensures that data analytics will be useful and meaningful? With this practical book, data architects and engineers will learn how to implement and deploy anonymization solutions within a data collection pipeline. You’ll establish and integrate secure, repeatable anonymization processes into your data flows and analytics in a sustainable manner.

Luk Arbuckle and Khaled El Emam from Privacy Analytics explore end-to-end solutions for anonymizing data, based on data collection models and use cases enabled by real business needs. These examples come from some of the most demanding data environments, using approaches that have stood the test of time.

Table of Contents

  1. Preface
    1. Audience
    2. Conventions Used in This Book
    3. Using Code Examples
    4. O’Reilly Online Learning
    5. How to Contact Us
    6. Acknowledgments
  2. 1. Introduction
    1. Our Motivation in Writing This Book
    2. Getting to Terms
      1. Regulations
      2. States of Data
    3. Anonymization as Data Protection
      1. Approval or Consent
      2. Purpose Specification
      3. Re-Identification Attacks
    4. Risk-Based Anonymization
    5. About This Book
  3. 2. Identifiability Spectrum
    1. Legal Landscape
    2. Disclosure Risk
      1. Types of Disclosure
      2. Dimensions of Data Privacy
    3. Re-Identification Science
      1. Defined Population
      2. Direction of Matching
      3. Structure of Data
    4. Re-Identification Risk
    5. Final Thoughts
  4. 3. A Practical Risk-Management Framework
    1. Five Safes of Anonymization
      1. Safe Projects
      2. Safe People
      3. Safe Settings
      4. Safe Data
      5. Safe Outputs
    2. Five Safes in Practice
    3. Final Thoughts
  5. 4. Identified Data
    1. Requirements Gathering
      1. Use Cases
      2. Data Flows
      3. Data and Data Subjects
    2. From Primary to Secondary Use
      1. Dealing with Direct Identifiers
      2. Dealing with Indirect Identifiers
      3. From Identified to Anonymized
      4. Mixing Identified with Anonymized
      5. Applying Anonymized to Identified
    3. Final Thoughts