000 | 04022nam a22005055i 4500 | ||
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001 | 978-3-031-02347-7 | ||
003 | DE-He213 | ||
005 | 20240730163910.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2016 sz | s |||| 0|eng d | ||
020 |
_a9783031023477 _9978-3-031-02347-7 |
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024 | 7 |
_a10.1007/978-3-031-02347-7 _2doi |
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050 | 4 | _aQA76.9.A25 | |
072 | 7 |
_aUR _2bicssc |
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_aUTN _2bicssc |
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_aCOM053000 _2bisacsh |
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072 | 7 |
_aUR _2thema |
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072 | 7 |
_aUTN _2thema |
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082 | 0 | 4 |
_a005.8 _223 |
100 | 1 |
_aDomingo-Ferrer, Josep. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981100 |
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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. |
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300 |
_aXV, 120 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Information Security, Privacy, and Trust, _x1945-9750 |
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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 |
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650 | 1 | 4 |
_aData and Information Security. _931990 |
700 | 1 |
_aSánchez, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981101 |
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700 | 1 |
_aSoria-Comas, Jordi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981102 |
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710 | 2 |
_aSpringerLink (Online service) _981103 |
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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 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02347-7 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c85105 _d85105 |