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001 978-3-031-02347-7
003 DE-He213
005 20240730163910.0
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008 220601s2016 sz | s |||| 0|eng d
020 _a9783031023477
_9978-3-031-02347-7
024 7 _a10.1007/978-3-031-02347-7
_2doi
050 4 _aQA76.9.A25
072 7 _aUR
_2bicssc
072 7 _aUTN
_2bicssc
072 7 _aCOM053000
_2bisacsh
072 7 _aUR
_2thema
072 7 _aUTN
_2thema
082 0 4 _a005.8
_223
100 1 _aDomingo-Ferrer, Josep.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981100
245 1 0 _aDatabase Anonymization
_h[electronic resource] :
_bPrivacy Models, Data Utility, and Microaggregation-based Inter-model Connections /
_cby Josep Domingo-Ferrer, David Sánchez, Jordi Soria-Comas.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 120 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Information Security, Privacy, and Trust,
_x1945-9750
505 0 _aPreface -- Acknowledgments -- Introduction -- Privacy in Data Releases -- Anonymization Methods for Microdata -- Quantifying Disclosure Risk: Record Linkage -- The k-Anonymity Privacy Model -- Beyond k-Anonymity: l-Diversity and t-Closeness -- t-Closeness Through Microaggregation -- Differential Privacy -- Differential Privacy by Multivariate Microaggregation -- Differential Privacy by Individual Ranking Microaggregation -- Conclusions and Research Directions -- Bibliography -- Authors' Biographies .
520 _aThe current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guaranteesthey offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
650 0 _aData protection.
_97245
650 1 4 _aData and Information Security.
_931990
700 1 _aSánchez, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981101
700 1 _aSoria-Comas, Jordi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981102
710 2 _aSpringerLink (Online service)
_981103
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031012198
776 0 8 _iPrinted edition:
_z9783031034756
830 0 _aSynthesis Lectures on Information Security, Privacy, and Trust,
_x1945-9750
_981104
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02347-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85105
_d85105