Introduction to Privacy-Preserving Data Publishing
eBook - PDF

Introduction to Privacy-Preserving Data Publishing

Concepts and Techniques

  1. 376 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction to Privacy-Preserving Data Publishing

Concepts and Techniques

About this book

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Int

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Yes, you can access Introduction to Privacy-Preserving Data Publishing by Benjamin C.M. Fung, Ke Wang, Ada Wai-Chee Fu, Philip S. Yu in PDF and/or ePUB format, as well as other popular books in Computer Science & Cyber Security. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Front cover
  2. Contents
  3. List of Figures
  4. List of Tables
  5. List of Algorithms
  6. Preface
  7. Acknowledgments
  8. About the Authors
  9. Part I: The Fundamentals
  10. Chapter 1. Introduction
  11. Chapter 2. Attack Models and Privacy Models
  12. Chapter 3. Anonymization Operations
  13. Chapter 4. Information Metrics
  14. Chapter 5. Anonymization Algorithms
  15. Part II: Anonymization for Data Mining
  16. Chapter 6. Anonymization for Classification Analysis: A Case Study on the Red Cross
  17. Chapter 7. Anonymization for Cluster Analysis
  18. Part III: Extended Data Publishing Scenarios
  19. Chapter 8. Multiple Views Publishing
  20. Chapter 9. Anonymizing Sequential Releases with New Attributes
  21. Chapter 10. Anonymizing Incrementially Updated Data Records
  22. Chapter 11. Collaborative Anonymization for Vertically Partitioned Data
  23. Chapter 12. Collaborative Anonymization for Horizontally Partitioned Data
  24. Part IV: Anonymizing Complex Data
  25. Chapter 13. Anonymizing Transaction Data
  26. Chapter 14. Anonymizing Trajectory Data
  27. Chapter 15. Anonymizing Social Networks
  28. Chapter 16. Sanitizing Textual Data
  29. Chapter 17. Other Privacy-Preserving Techniques and Future Trends
  30. References
  31. Back cover