ISBN: 9783031128363 - guide to data privacy: models, technologies, solutions (undergraduate topics in computer science) (26 resultados)

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Paperback. Condición: New. 1st ed. 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 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 Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.…

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 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.…

Taschenbuch. Condición: Neu. Guide to Data Privacy | Models, Technologies, Solutions | Vicenç Torra | Taschenbuch | Undergraduate Topics in Computer Science | xvi | Englisch | 2022 | Springer | EAN 9783031128363 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: New. 1st ed. 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 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 Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.…

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 332 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 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 Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 332 pp. Englisch.…