Central Library

Normal view MARC view ISBD view

Differential Privacy for Dynamic Data [electronic resource] / by Jerome Le Ny.

By: Le Ny, Jerome [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Control, Automation and Robotics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XI, 110 p. 14 illus., 9 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030410391.Subject(s): Signal processing | Data protection | Control engineering | Information retrieval | Computer architecture | Signal, Speech and Image Processing | Data and Information Security | Control and Systems Theory | Data Storage RepresentationAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

There are no comments for this item.

Log in to your account to post a comment.