| 000 | 03420nam a22005895i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-41039-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214620.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 200324s2020 sz | s |||| 0|eng d | ||
| 020 |
_a9783030410391 _9978-3-030-41039-1 |
||
| 024 | 7 |
_a10.1007/978-3-030-41039-1 _2doi |
|
| 050 | 4 | _aTK5102.9 | |
| 072 | 7 |
_aTJF _2bicssc |
|
| 072 | 7 |
_aUYS _2bicssc |
|
| 072 | 7 |
_aTEC008000 _2bisacsh |
|
| 072 | 7 |
_aTJF _2thema |
|
| 072 | 7 |
_aUYS _2thema |
|
| 082 | 0 | 4 |
_a621.382 _223 |
| 100 | 1 |
_aLe Ny, Jerome. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _939394 |
|
| 245 | 1 | 0 |
_aDifferential Privacy for Dynamic Data _h[electronic resource] / _cby Jerome Le Ny. |
| 250 | _a1st ed. 2020. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
|
| 300 |
_aXI, 110 p. 14 illus., 9 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSpringerBriefs in Control, Automation and Robotics, _x2192-6794 |
|
| 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 | _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. | ||
| 650 | 0 |
_aSignal processing. _94052 |
|
| 650 | 0 |
_aData protection. _97245 |
|
| 650 | 0 |
_aControl engineering. _931970 |
|
| 650 | 0 |
_aInformation retrieval. _910134 |
|
| 650 | 0 |
_aComputer architecture. _93513 |
|
| 650 | 1 | 4 |
_aSignal, Speech and Image Processing . _931566 |
| 650 | 2 | 4 |
_aData and Information Security. _931990 |
| 650 | 2 | 4 |
_aControl and Systems Theory. _931972 |
| 650 | 2 | 4 |
_aData Storage Representation. _931576 |
| 710 | 2 |
_aSpringerLink (Online service) _939395 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030410384 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030410407 |
| 830 | 0 |
_aSpringerBriefs in Control, Automation and Robotics, _x2192-6794 _939396 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-41039-1 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c76549 _d76549 |
||