Building an Anonymization Pipeline. Creating Safe Data - Helion

ebook

Autor: Luk Arbuckle, Khaled El Emam
ISBN: 978-14-920-5338-5
stron: 166, Format: ebook
Data wydania: 2020-04-13
Księgarnia: Helion

Cena książki: 152,15 zł (poprzednio: 176,92 zł)
Oszczędzasz: 14% (-24,77 zł)

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How can you use data in a way that protects individual privacy but still provides useful and meaningful analytics? With this practical book, data architects and engineers will learn how to establish and integrate secure, repeatable anonymization processes into their data flows and analytics in a sustainable manner.

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

Create anonymization solutions diverse enough to cover a spectrum of use cases
Match your solutions to the data you use, the people you share it with, and your analysis goals
Build anonymization pipelines around various data collection models to cover different business needs
Generate an anonymized version of original data or use an analytics platform to generate anonymized outputs
Examine the ethical issues around the use of anonymized data

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Spis treści

Building an Anonymization Pipeline. Creating Safe Data eBook -- spis treści

Preface
- Why We Wrote This Book
- Who This Book Was Written For
- How This Book Is Organized
- Conventions Used in This Book
- OReilly 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
    - AOL search queries
    - Netflix Prize
    - State Inpatient Database
    - Lessons learned
- Anonymization in Practice
- Final Thoughts
2. Identifiability Spectrum
- Legal Landscape
- Disclosure Risk
  - Types of Disclosure
    - Learning something new
  - Dimensions of Data Privacy
    - Linkability
    - Addressability
    - Identifiability
    - Inference
- Re-identification Science
  - Defined Population
  - Direction of Matching
    - Sample to population (public)
    - Population to sample (acquaintance)
  - Structure of Data
    - Cross-sectional data
    - Time-series data
    - Longitudinal or panel data
    - Multilevel or hierarchical data
- Overall Identifiability
- Final Thoughts
3. A Practical Risk-Management Framework
- Five Safes of Anonymization
  - Safe Projects
    - Primary and secondary purposes
    - When to anonymize
  - Safe People
    - Recipient trust
    - Acquaintances
  - Safe Settings
    - Risk matrix
  - Safe Data
    - Quantifying identifiability
  - Safe Outputs
    - Invasion of privacy
- Five Safes in Practice
- Final Thoughts
4. Identified Data
- Requirements Gathering
  - Use Cases
  - Data Flows
  - Data and Data Subjects
    - Data subjects
    - Structure and properties of the data
    - Categories of information
- From Primary to Secondary Use
  - Dealing with Direct Identifiers
    - Realistic direct identifiers
  - Dealing with Indirect Identifiers
  - From Identified to Anonymized
    - Data (anonymization) processors
    - Controlled re-identification
  - Mixing Identified with Anonymized
    - Functionally anonymized
    - Five Safes as an information barrier
  - 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
    - Remote analysis
    - Secure computation
  - Synthetic Data
    - Differential privacy
  - Biometric Identifiers
    - Secure computation of genomic data
- 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
    - Classical machine learning
    - Neural networks
  - Technical Challenges
  - Algorithms Failing on Trust
    - Rogue chatbot
    - Predicting criminality
- Principles of Responsible AIML
- Governance and Oversight
  - Privacy Ethics
  - Data Monitoring
- Final Thoughts
Index