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Guide to Data Privacy: Models, Technologies, Solutions de Vicenç Torra

Guide to Data Privacy: Models, Technologies, Solutions: Undergraduate Topics in Computer Science

Autor Vicenç Torra
en Limba Engleză Paperback – 5 noi 2022
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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Specificații

ISBN-13: 9783031128363
ISBN-10: 3031128362
Pagini: 313
Ilustrații: XVI, 313 p. 33 illus., 6 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.51 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Undergraduate Topics in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

1. Introduction.- 2. Basics of Cryptography and Machine Learning.- 3. Privacy Models and Privacy Mechanisms.- 4. User's Privacy.- 5. Avoiding Disclosure from Computations.- 6. Avoiding Disclosure from Data Masking Methods.- 7. Other.- 8. Conclusions.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

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