Condizione: As New. Unread book in perfect condition.
Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2016
ISBN 10: 3031012224 ISBN 13: 9783031012228
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 47,90
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Aggiungi al carrelloPaperback. Condizione: New. Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 34,60
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Aggiungi al carrelloCondizione: New. In English.
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Aggiungi al carrelloCondizione: New.
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
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Da: California Books, Miami, FL, U.S.A.
EUR 74,33
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Aggiungi al carrelloCondizione: New.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2016
ISBN 10: 3031012224 ISBN 13: 9783031012228
Da: Rarewaves.com UK, London, Regno Unito
Prima edizione
EUR 37,53
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Aggiungi al carrelloPaperback. Condizione: New. 1st. Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.
Da: Majestic Books, Hounslow, Regno Unito
EUR 82,48
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 84,83
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2016
ISBN 10: 3031012224 ISBN 13: 9783031012228
Da: moluna, Greven, Germania
EUR 60,06
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data an.