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Guide to Data Privacy

Models, Technologies, Solutions

  • Textbook
  • © 2022

Overview

  • Presents the main privacy models and the main technologies
  • Describes some of the most relevant algorithms
  • Offers characterization, comparison, and examples of privacy models

Part of the book series: Undergraduate Topics in Computer Science (UTICS)

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About this book

Data privacy technologies are essential for implementing information systems with privacy by design.

Privacy technologies clearly are needed for ensuring that data does not lead to disclosure, but also that statistics or even data-driven machine learning models do not lead to disclosure.  For example, can a deep-learning model be attacked to discover that sensitive data has been used for its training?  This accessible textbook presents privacy models, computational definitions of privacy, and methods to implement them. Additionally, the book explains and gives plentiful examples of how to implement—among other models—differential privacy, k-anonymity, and secure multiparty computation.

Topics and features:

  • Provides integrated presentation of data privacy (including tools from statistical disclosure control, privacy-preserving data mining, and privacy for communications)
  • Discusses privacy requirements and tools fordifferent types of scenarios, including privacy for data, for computations, and for users
  • Offers characterization of privacy models, comparing their differences, advantages, and disadvantages
  • Describes some of the most relevant algorithms to implement privacy models
  • Includes examples of data protection mechanisms

This unique textbook/guide contains numerous examples and succinctly and comprehensively gathers the relevant information. As such, it will be eminently suitable for undergraduate and graduate students interested in data privacy, as well as professionals wanting a concise overview.

Vicenç Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.

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Keywords

Table of contents (9 chapters)

Authors and Affiliations

  • Department of Computing Science, Umeå University, Umeå, Sweden

    Vicenç Torra

About the author

Vicenç Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.  He is the Wallenberg Chair on AI at the university, as well as a fellow of IEEE and EurAI.

Bibliographic Information

  • Book Title: Guide to Data Privacy

  • Book Subtitle: Models, Technologies, Solutions

  • Authors: Vicenç Torra

  • Series Title: Undergraduate Topics in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-12837-0

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-031-12836-3Published: 05 November 2022

  • eBook ISBN: 978-3-031-12837-0Published: 04 November 2022

  • Series ISSN: 1863-7310

  • Series E-ISSN: 2197-1781

  • Edition Number: 1

  • Number of Pages: XVI, 313

  • Number of Illustrations: 27 b/w illustrations, 6 illustrations in colour

  • Topics: Privacy, Systems and Data Security, Cryptology, Ethics, Computers and Society

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