000 04019nam a22006135i 4500
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
024 7 _a10.1007/978-3-031-12837-0
_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 _aTorra, Vicenç.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXVI, 313 p. 33 illus., 6 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
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