000			03071nam a22004215i 4500
999			_c383207 _d383207 _x1
001			383207
003			ES-MaUEC
005			20230102122053.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			221119s2022 sz | s |||| 0|eng d
020			_a9783030963989
024	7		_a10.1007/978-3-030-96398-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A25 _b2022 EB
100	1		_aPejó, Balázs _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685392
245	1	0	_aGuide to Differential Privacy Modifications : _ba Taxonomy of Variants and Extensions _cby Balázs Pejó, Damien Desfontaines
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (VIII, 89 páginas) _b2 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_a1. Introduction -- 2. Differential Privacy -- 3. Quantification of privacy loss -- 4. Neighborhood definition (N) -- 5. Variation of privacy loss (V) -- 6. Background knowledge (B) -- 7. Change in formalism (F) -- 8. Relativization of the knowledge gain (R) -- 9. Computational power (C) -- 10. Summarizing table -- 11. Scope and related work -- 12. Conclusion.
520			_aShortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified. These categories act like dimensions: Variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.
988			_aSpringer_Computer_2022
650		7	_2embne _9158200 _aSeguridad informática
700	1		_aDesfontaines, Damien _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685393
776	0	8	_iPrinted edition: _z9783030963972
776	0	8	_iPrinted edition: _z9783030963996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-96398-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI