Building an Anonymization Pipeline: Creating safe data 9781492053439

How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With

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English Year 2020

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Building an Anonymization Pipeline: Creating safe data
 9781492053439

Table of contents :
Preface
Why We Wrote This Book
Who This Book Was Written For
How This Book Is Organized
Conventions Used in This Book
O’Reilly Online Learning
How to Contact Us
Acknowledgments
1. Introduction
Identifiability
Getting to Terms
Laws and Regulations
States of Data
Anonymization as Data Protection
Approval or Consent
Purpose Specification
Re-identification Attacks
Anonymization in Practice
Final Thoughts
2. Identifiability Spectrum
Legal Landscape
Disclosure Risk
Types of Disclosure
Dimensions of Data Privacy
Re-identification Science
Defined Population
Direction of Matching
Structure of Data
Overall Identifiability
Final Thoughts
3. A Practical Risk-Management Framework
Five Safes of Anonymization
Safe Projects
Safe People
Safe Settings
Safe Data
Safe Outputs
Five Safes in Practice
Final Thoughts
4. Identified Data
Requirements Gathering
Use Cases
Data Flows
Data and Data Subjects
From Primary to Secondary Use
Dealing with Direct Identifiers
Dealing with Indirect Identifiers
From Identified to Anonymized
Mixing Identified with Anonymized
Applying Anonymized to Identified
Final Thoughts
5. Pseudonymized Data
Data Protection and Legal Authority
Pseudonymized Services
Legal Authority
Legitimate Interests
A First Step to Anonymization
Revisiting Primary to Secondary Use
Analytics Platforms
Synthetic Data
Biometric Identifiers
Final Thoughts
6. Anonymized Data
Identifiability Spectrum Revisited
Making the Connection
Anonymized at Source
Additional Sources of Data
Pooling Anonymized Data
Pros/Cons of Collecting at Source
Methods of Collecting at Source
Safe Pooling
Access to the Stored Data
Feeding Source Anonymization
Final Thoughts
7. Safe Use
Foundations of Trust
Trust in Algorithms
Techniques of AIML
Technical Challenges
Algorithms Failing on Trust
Principles of Responsible AIML
Governance and Oversight
Privacy Ethics
Data Monitoring
Final Thoughts
Index

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