Introduction to Privacy-Preserving Data Publishing : Concepts and Techniques

Benjamin C. M. Fung

ISBN 10: 0367383756 / ISBN 13: 9780367383756

Published by Chapman And Hall/CRC, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 'Introduction to Privacy-Preserving Data Publishing' presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements.

Seller Inventory # 9780367383756

Bibliographic Details

• Title: Introduction to Privacy-Preserving Data Publishing : Concepts and Techniques
• Publisher: Chapman And Hall/CRC
• Publication Date: 2019
• Language: English
• ISBN 10: 0367383756
• ISBN 13: 9780367383756
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 20D millimeters 234 millimeters width by 156 millimeters height by 20 millimeters depth
• Weight: 573 grams

About this title

Synopsis

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.

About the Author

Benjamin C. M. Fung is an assistant professor in the Concordia Institute for Information Systems Engineering at Concordia University in Montreal, Quebec. Dr. Fung is also a research scientist and the treasurer of the National Cyber-Forensics and Training Alliance Canada (NCFTA Canada).

Ke Wang is a professor in the School of Computing Science at Simon Fraser University in Burnaby, British Columbia.

Ada Wai-Chee Fu is an associate professor in the Department of Computer Science and Engineering at the Chinese University of Hong Kong.

Philip S. Yu is a professor in the Department of Computer Science and the Wexler Chair in Information and Technology at the University of Illinois at Chicago.