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. Introduction to
Privacy-Preserving Data Publishing: Concepts and Techniques
presents state-of-the-art information sharing and data integration
methods that take into account privacy and data mining
requirements.
The first part of the book discusses the fundamentals of the
field. In the second part, the authors present anonymization
methods for preserving information utility for specific data mining
tasks. The third part examines the privacy issues, privacy models,
and anonymization methods for realistic and challenging data
publishing scenarios. While the first three parts focus on
anonymizing relational data, the last part studies the privacy
threats, privacy models, and anonymization methods for complex
data, including transaction, trajectory, social network, and
textual data.
This book not only explores privacy and information utility
issues but also efficiency and scalability challenges. In many
chapters, the authors highlight efficient and scalable methods and
provide an analytical discussion to compare the strengths and
weaknesses of different solutions.
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