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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 26,22
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 25,89
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 29,00
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Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
EUR 47,20
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EUR 49,28
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Da: California Books, Miami, FL, U.S.A.
EUR 49,65
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Da: ALLBOOKS1, Direk, SA, Australia
EUR 51,78
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Da: ALLBOOKS1, Direk, SA, Australia
EUR 51,78
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Da: ALLBOOKS1, Direk, SA, Australia
EUR 52,94
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Da: UK BOOKS STORE, London, LONDO, Regno Unito
EUR 73,81
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Aggiungi al carrelloPapeback. Condizione: New. Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Editore: Springer-Nature New York Inc, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 65,88
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Aggiungi al carrelloPaperback. Condizione: Brand New. 329 pages. 9.25x6.10x0.69 inches. In Stock.
Editore: Springer International Publishing, 2022
ISBN 10: 3031128362 ISBN 13: 9783031128363
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 42,80
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Aggiungi al carrelloTaschenbuch. Condizione: 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.