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Differential privacy for dynamic data

Le Ny, Jerome.

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Differential privacy for dynamic data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Differential privacy for dynamic data/ by Jerome Le Ny.
Author:	Le Ny, Jerome.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xi, 110 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Defining Privacy Preserving Data Analysis -- Chapter 2. Basic Differentially Private Mechanism -- Chapter 3. A Two-Stage Architecture for Differentially Private Filtering -- Chapter 4. Differentially Private Filtering for Stationary Stochastic Collective Signals -- Chapter 5. Differentially Private Kalman Filtering -- Chapter 6. Differentially Private Nonlinear Observers -- Chapter 7. Conclusion.
Contained By:	Springer eBooks
Subject:	Data protection. -
Online resource:	https://doi.org/10.1007/978-3-030-41039-1
ISBN:	9783030410391

Differential privacy for dynamic data
Le Ny, Jerome.

Differential privacy for dynamic data[electronic resource] /by Jerome Le Ny. - Cham :Springer International Publishing :2020. - xi, 110 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering. Control, automation and robotics. - SpringerBriefs in electrical and computer engineering.Control, automation and robotics..

Chapter 1. Defining Privacy Preserving Data Analysis -- Chapter 2. Basic Differentially Private Mechanism -- Chapter 3. A Two-Stage Architecture for Differentially Private Filtering -- Chapter 4. Differentially Private Filtering for Stationary Stochastic Collective Signals -- Chapter 5. Differentially Private Kalman Filtering -- Chapter 6. Differentially Private Nonlinear Observers -- Chapter 7. Conclusion.

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.

ISBN: 9783030410391

Standard No.: 10.1007/978-3-030-41039-1doiSubjects--Topical Terms:

590548
Data protection.

LC Class. No.: QA76.9.A25 / L469 2020

Dewey Class. No.: 005.8

Differential privacy for dynamic data
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245 1 0 $a Differential privacy for dynamic data $h [electronic resource] / $c by Jerome Le Ny.
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300 $a xi, 110 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering. Control, automation and robotics
505 0 $a Chapter 1. Defining Privacy Preserving Data Analysis -- Chapter 2. Basic Differentially Private Mechanism -- Chapter 3. A Two-Stage Architecture for Differentially Private Filtering -- Chapter 4. Differentially Private Filtering for Stationary Stochastic Collective Signals -- Chapter 5. Differentially Private Kalman Filtering -- Chapter 6. Differentially Private Nonlinear Observers -- Chapter 7. Conclusion.
520 $a 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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650 2 4 $a Systems and Data Security. $3 898223
650 2 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Data Storage Representation. $3 892664
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830 0 $a SpringerBriefs in electrical and computer engineering. $p Control, automation and robotics. $3 1622177
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