000 | 04019nam a22006135i 4500 | ||
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001 | 978-3-031-12837-0 | ||
003 | DE-He213 | ||
005 | 20240423130109.0 | ||
007 | cr nn 008mamaa | ||
008 | 221104s2022 sz | s |||| 0|eng d | ||
020 |
_a9783031128370 _9978-3-031-12837-0 |
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024 | 7 |
_a10.1007/978-3-031-12837-0 _2doi |
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050 | 4 | _aQA76.9.A25 | |
050 | 4 | _aJC596-596.2 | |
072 | 7 |
_aURD _2bicssc |
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072 | 7 |
_aCOM060040 _2bisacsh |
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072 | 7 |
_aURD _2thema |
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082 | 0 | 4 |
_a005.8 _223 |
082 | 0 | 4 |
_a323.448 _223 |
100 | 1 |
_aTorra, Vicenç. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aGuide to Data Privacy _h[electronic resource] : _bModels, Technologies, Solutions / _cby Vicenç Torra. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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300 |
_aXVI, 313 p. 33 illus., 6 illus. in color. _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 |
_aUndergraduate Topics in Computer Science, _x2197-1781 |
|
505 | 0 | _a1. Introduction -- 2. Basics of Cryptography and Machine Learning -- 3. Privacy Models and Privacy Mechanisms -- 4. User's Privacy -- 5. Avoiding Disclosure from Computations -- 6. Avoiding Disclosure from Data Masking Methods -- 7. Other -- 8. Conclusions. | |
520 | _aData privacy technologies are essential for implementing information systems with privacy by design. Privacy technologies clearly are needed for ensuring that data does not lead to disclosure, but also that statistics or even data-driven machine learning models do not lead to disclosure. For example, can a deep-learning model be attacked to discover that sensitive data has been used for its training? This accessible textbook presents privacy models, computational definitions of privacy, and methods to implement them. Additionally, the book explains and gives plentiful examples of how to implement—among other models—differential privacy, k-anonymity, and secure multiparty computation. Topics and features: Provides integrated presentation of data privacy (including tools from statistical disclosure control, privacy-preserving data mining, and privacy for communications) Discusses privacy requirements and tools for different types of scenarios, including privacy for data, for computations, and for users Offers characterization of privacy models, comparing their differences, advantages, and disadvantages Describes some of the most relevant algorithms to implement privacy models Includes examples of data protection mechanisms This unique textbook/guide contains numerous examples and succinctly and comprehensively gathers the relevant information. As such, it will be eminently suitable for undergraduate and graduate students interested in data privacy, as well as professionals wanting a concise overview. Vicenç Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden. | ||
650 | 0 |
_aData protection _xLaw and legislation. |
|
650 | 0 | _aData protection. | |
650 | 0 | _aCryptography. | |
650 | 0 | _aData encryption (Computer science). | |
650 | 0 |
_aInformation technology _xMoral and ethical aspects. |
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650 | 0 | _aComputers and civilization. | |
650 | 1 | 4 | _aPrivacy. |
650 | 2 | 4 | _aData and Information Security. |
650 | 2 | 4 | _aCryptology. |
650 | 2 | 4 | _aInformation Ethics. |
650 | 2 | 4 | _aComputers and Society. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031128363 |
776 | 0 | 8 |
_iPrinted edition: _z9783031128387 |
830 | 0 |
_aUndergraduate Topics in Computer Science, _x2197-1781 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-12837-0 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185016 _d185016 |