Guide to Data Privacy: Models, Technologies, Solutions (Undergraduate Topics in Computer Science) - Softcover

Torra, Vicenç

Softcover

ISBN 10: 3031128362 ISBN 13: 9783031128363

Verlag: Springer, 2022

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

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.

Von der hinteren Coverseite

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.

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

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2022
• Sprache: Englisch
• ISBN 10: 3031128362
• ISBN 13: 9783031128363
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 332
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland