000			03038nam a2200445 i 4500
710	2		_aSpringerLink (Online service) _9106996
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008			200324s2020 gw | s |||| 0|eng d
020			_a9783030410391
024	7		_a10.1007/978-3-030-41039-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A25 _b2020 EB
100	1		_aLe Ny, Jerome _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673428
245	1	0	_aDifferential Privacy for Dynamic Data _cby Jerome Le Ny.
250			_aFirst edition 2020.
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XI, 110 páginas) _b14 ilustraciones, 9 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aSpringerBriefs in Control Automation and Robotics _x2192-6786
490	0		_aEngineering (Springer-11647)
505	0		_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.
520	3		_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.
988			_aSpringer_Engineering_31032020
650		7	_2embne _9147793 _aProtección de datos
650		7	_2embne _aProcesos estocásticos _9405190
650		7	_2embne _9140996 _aDerecho a la intimidad
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030410384
776	0	8	_iPrinted edition: _z9783030410407
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-41039-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI