Differential Privacy for Dynamic Data (SpringerBriefs in Control, Automation and Robotics) - Tapa blanda

Le Ny, Jerome

 
9783030410384: Differential Privacy for Dynamic Data (SpringerBriefs in Control, Automation and Robotics)
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ISBN 10: 3030410382 ISBN 13: 9783030410384
Editorial: Springer, 2020

Sinopsis

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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Acerca del autor

Jerome Le Ny is currently an Associate Professor in the Department of Electrical Engineering at Polytechnique Montreal, which he joined in May 2012, and a member of GERAD, a Canadian multi-university research center for decision systems. His research interests include control theory and stochastic signal processing, privacy and security for cyber-physical systems, and the design of large-scale intelligent automation, with applications to transportation systems, energy systems, and distributed robotics in particular. He has published papers on the topic of privacy-preserving dynamic data analysis since 2012 and co-organized invited sessions on the topic of privacy in systems and control at several Conference on Decision and Control (CDC) since then. He participated in a tutorial session on this topic at the CDC 2016. His group has received several research awards, including a best student paper award at CDC 2016, and a fellowship from the Humboldt Foundation in 2018.

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Editorial
Springer
Año de publicación
2020
Idioma
Inglés
ISBN 10 
3030410382
ISBN 13 
9783030410384
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
124
Contacto del fabricante
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3,69115 Heidelberg, Germany
Heidelberg
69115
Alemania

Otras ediciones populares con el mismo título

9783030410407: Differential Privacy for Dynamic Data

Edición Destacada

ISBN 10:  3030410404 ISBN 13:  9783030410407
Editorial: Springer, 2020
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