ISBN: 9781032028033 - handbook of sharing confidential data: differential privacy, secure multiparty computation, and synthetic data (19 risultati)

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Hardback. Condizione: New. Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature-specifically, synthetic data, formal privacy, and secure computation-can be used to manage trade-offs in disclosure risk and data usefulness.Key features. Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation. Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives. Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy. Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approachesThe handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.…

Hardcover. Condizione: Brand New. 376 pages. 10.00x7.00x10.00 inches. In Stock.

Hardback. Condizione: New. Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature-specifically, synthetic data, formal privacy, and secure computation-can be used to manage trade-offs in disclosure risk and data usefulness.Key features. Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation. Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives. Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy. Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approachesThe handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature-specifically, synthetic data, formal privacy, and secure computation-can be used to manage trade-offs in disclosure risk and data usefulness.Key features:- Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation- Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives- Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy- Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approachesThe handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis. 342 pp. Englisch.…

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Joerg Drechsler is Head of the Department for Statistical Methods at the Institute for Employment Research in Nuremberg, Germany, and Professor of Statistical Science at the Institute for Statistics at the Ludwig-Maximilians-University in.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature-specifically, synthetic data, formal privacy, and secure computation-can be used to manage trade-offs in disclosure risk and data usefulness.Key features:- Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation- Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives- Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy- Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approachesThe handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.…