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ISBN 10 : 3030410382 ISBN 13 : 9783030410384
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ISBN 10 : 3030410382 ISBN 13 : 9783030410384
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Ajouter au panierPaperback. Etat : New. 2020 ed. This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
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Ajouter au panierPaperback. Etat : Brand New. 124 pages. 9.25x6.10x0.39 inches. In Stock.
Edité par Springer International Publishing, 2020
ISBN 10 : 3030410382 ISBN 13 : 9783030410384
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Ajouter au panierEtat : New. Introduces readers to the emerging topic of privacy in the context of signal processing and control systems Illustrates concepts, with case studies that analyze real-world public datasetsProvides a syst.
Edité par Springer International Publishing, Springer International Publishing Mär 2020, 2020
ISBN 10 : 3030410382 ISBN 13 : 9783030410384
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch.
Edité par Springer International Publishing, Springer International Publishing, 2020
ISBN 10 : 3030410382 ISBN 13 : 9783030410384
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing.For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
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Ajouter au panierTaschenbuch. Etat : Neu. Differential Privacy for Dynamic Data | Jerome Le Ny | Taschenbuch | xi | Englisch | 2020 | Springer | EAN 9783030410384 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Edité par Springer Nature Switzerland AG, CH, 2020
ISBN 10 : 3030410382 ISBN 13 : 9783030410384
Langue: anglais
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Ajouter au panierPaperback. Etat : New. 2020 ed. This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.
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Ajouter au panierPaperback. Etat : Brand New. 124 pages. 9.25x6.10x0.39 inches. In Stock. This item is printed on demand.
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ISBN 10 : 3030410382 ISBN 13 : 9783030410384
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing.For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks. 124 pp. Englisch.
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Ajouter au panierEtat : New. Print on Demand pp. 110.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 110.