Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Title
Differential privacy for dynamic data / Jerome Le Ny.
ISBN
9783030410391 (electronic book)
3030410390 (electronic book)
3030410382
9783030410384
Published
Cham : Springer, [2020]
Copyright
©2020
Language
English
Description
1 online resource.
Call Number
QA76.9.A25
Dewey Decimal Classification
005.8
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 9, 2020).
Series
SpringerBriefs in electrical and computer engineering. Control, automation and robotics.
Available in Other Form
Print version: 9783030410384
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.
