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Kategorie szczegółowe BISAC

Guide to Data Privacy: Models, Technologies, Solutions

ISBN-13: 9783031128363 / Angielski / Miękka / 2022 / 313 str.

Vicenç Torra
Guide to Data Privacy: Models, Technologies, Solutions Vicen? Torra 9783031128363 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Guide to Data Privacy: Models, Technologies, Solutions

ISBN-13: 9783031128363 / Angielski / Miękka / 2022 / 313 str.

Vicenç Torra
cena 161,41
(netto: 153,72 VAT:  5%)

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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 usersOffers characterization of privacy models, comparing their differences, advantages, and disadvantagesDescribes some of the most relevant algorithms to implement privacy modelsIncludes examples of data protection mechanismsThis 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 Torrais Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.

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.

Kategorie:
Informatyka, Bazy danych
Kategorie BISAC:
Computers > Internet - Online Safety & Privacy
Computers > Security - Cryptography & Encryption
Philosophy > Ethics & Moral Philosophy
Wydawca:
Springer
Seria wydawnicza:
Undergraduate Topics in Computer Science
Język:
Angielski
ISBN-13:
9783031128363
Rok wydania:
2022
Dostępne języki:
Numer serii:
000343004
Ilość stron:
313
Waga:
0.46 kg
Wymiary:
23.39 x 15.6 x 1.75
Oprawa:
Miękka
Dodatkowe informacje:
Wydanie ilustrowane

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.

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.

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.



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