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000930214 019__ $$a1148893612
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000930214 020__ $$a3030410390$$q(electronic book)
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000930214 1001_ $$aLe Ny, Jérôme,$$eauthor.
000930214 24510 $$aDifferential privacy for dynamic data /$$cJerome Le Ny.
000930214 264_1 $$aCham :$$bSpringer,$$c[2020]
000930214 264_4 $$c©2020
000930214 300__ $$a1 online resource.
000930214 336__ $$atext$$btxt$$2rdacontent
000930214 337__ $$acomputer$$bc$$2rdamedia
000930214 338__ $$aonline resource$$bcr$$2rdacarrier
000930214 4901_ $$aSpringerBriefs in electrical and computer engineering. Control, automation and robotics
000930214 504__ $$aIncludes bibliographical references.
000930214 5050_ $$aChapter 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.
000930214 506__ $$aAccess limited to authorized users.
000930214 520__ $$aThis 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.
000930214 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 9, 2020).
000930214 650_0 $$aData protection.
000930214 650_0 $$aFilters (Mathematics)
000930214 650_0 $$aStochastic processes.
000930214 650_0 $$aPrivacy, Right of.
000930214 77608 $$iPrint version:$$z3030410382$$z9783030410384$$w(OCoLC)1137840303
000930214 830_0 $$aSpringerBriefs in electrical and computer engineering.$$pControl, automation and robotics.
000930214 852__ $$bebk
000930214 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-41039-1$$zOnline Access$$91397441.1
000930214 909CO $$ooai:library.usi.edu:930214$$pGLOBAL_SET
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000930214 980__ $$aBIB
000930214 982__ $$aEbook
000930214 983__ $$aOnline
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