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020 _a9783030963989
_9978-3-030-96398-9
024 7 _a10.1007/978-3-030-96398-9
_2doi
050 4 _aQA76.9.A25
050 4 _aJC596-596.2
072 7 _aURD
_2bicssc
072 7 _aCOM060040
_2bisacsh
072 7 _aURD
_2thema
082 0 4 _a005.8
_223
082 0 4 _a323.448
_223
100 1 _aPejó, Balázs.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGuide to Differential Privacy Modifications
_h[electronic resource] :
_bA Taxonomy of Variants and Extensions /
_cby Balázs Pejó, Damien Desfontaines.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aVIII, 89 p. 2 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 collectingexisting ones.
650 0 _aData protection
_xLaw and legislation.
650 0 _aData protection.
650 0 _aCryptography.
650 0 _aData encryption (Computer science).
650 1 4 _aPrivacy.
650 2 4 _aData and Information Security.
650 2 4 _aCryptology.
700 1 _aDesfontaines, Damien.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030963972
776 0 8 _iPrinted edition:
_z9783030963996
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-3-030-96398-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c179204
_d179204