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Differential privacy for dynamic data / Jerome Le Ny.

9783030410391 (electronic book)
3030410390 (electronic book)
3030410382
9783030410384

Cham : Springer, [2020]

©2020

English

1 online resource.

QA76.9.A25

005.8

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.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed April 9, 2020).

SpringerBriefs in electrical and computer engineering. Control, automation and robotics.

Print version: 9783030410384

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