Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: As New. Unread book in perfect condition.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: Basi6 International, Irving, TX, USA
Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: GreatBookPricesUK, Castle Donington, DERBY, Vereinigtes Königreich
Zustand: New.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: GreatBookPricesUK, Castle Donington, DERBY, Vereinigtes Königreich
Zustand: As New. Unread book in perfect condition.
Verlag: Springer, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: California Books, Miami, FL, USA
Zustand: New.
Verlag: Springer 2022-11, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
PF. Zustand: New.
Verlag: Springer-Nature New York Inc, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Paperback. Zustand: Brand New. 329 pages. 9.25x6.10x0.69 inches. In Stock.
Verlag: Springer International Publishing, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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.