Details

Title Guide to Data Privacy: Models, Technologies, Solutions
Author Torra, Vicenc
Binding Paperback
Condition New
Publisher Springer-Nature New York Inc
Publication date 2022
Features Illustrated
Bookseller's Inventory # x-3031128362
ISBN 9783031128363
Quantity available 2

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From the publisher

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.

Vicen Torra is Professor with the Department of Computing Science at Ume University, Ume, Sweden.

From the rear cover

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 for different 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.

Vicen Torra is Professor with the Department of Computing Science at Ume University, Ume, Sweden.

About the author

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

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