001451233 000__ 03711cam\a2200505\i\4500
001451233 001__ 1451233
001451233 003__ OCoLC
001451233 005__ 20230310004649.0
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001451233 008__ 221116s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001451233 019__ $$a1350511200$$a1350687943
001451233 020__ $$a9783031128370$$q(electronic bk.)
001451233 020__ $$a3031128370$$q(electronic bk.)
001451233 020__ $$z9783031128363
001451233 020__ $$z3031128362
001451233 0247_ $$a10.1007/978-3-031-12837-0$$2doi
001451233 035__ $$aSP(OCoLC)1350842613
001451233 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dN$T$$dSFB$$dOCLCF$$dUKAHL$$dOCLCQ
001451233 049__ $$aISEA
001451233 050_4 $$aQA76.9.A25
001451233 08204 $$a005.8$$223/eng/20221116
001451233 1001_ $$aTorra, Vicenç,$$eauthor.$$1https://isni.org/isni/0000000063030902
001451233 24510 $$aGuide to data privacy :$$bmodels, technologies, solutions /$$cVicenç Torra.
001451233 264_1 $$aCham :$$bSpringer,$$c2022.
001451233 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and color).
001451233 336__ $$atext$$btxt$$2rdacontent
001451233 337__ $$acomputer$$bc$$2rdamedia
001451233 338__ $$aonline resource$$bcr$$2rdacarrier
001451233 4901_ $$aUndergraduate topics in computer science
001451233 5050_ $$a1. Introduction -- 2. Basics of Cryptography and Machine Learning -- 3. Privacy Models and Privacy Mechanisms -- 4. User's Privacy -- 5. Avoiding Disclosure from Computations -- 6. Avoiding Disclosure from Data Masking Methods -- 7. Other -- 8. Conclusions.
001451233 506__ $$aAccess limited to authorized users.
001451233 520__ $$aData privacy technologies are essential for implementing information systems with privacy by design. Privacy technologies clearly are needed for ensuring that data does not lead to disclosure, but also that statistics or even data-driven machine learning models do not lead to disclosure. For example, can a deep-learning model be attacked to discover that sensitive data has been used for its training? This accessible textbook presents privacy models, computational definitions of privacy, and methods to implement them. Additionally, the book explains and gives plentiful examples of how to implementamong other modelsdifferential privacy, k-anonymity, and secure multiparty computation. Topics and features: Provides integrated presentation of data privacy (including tools from statistical disclosure control, privacy-preserving data mining, and privacy for communications) Discusses privacy requirements and tools for different types of scenarios, including privacy for data, for computations, and for users Offers characterization of privacy models, comparing their differences, advantages, and disadvantages Describes some of the most relevant algorithms to implement privacy models Includes examples of data protection mechanisms This unique textbook/guide contains numerous examples and succinctly and comprehensively gathers the relevant information. As such, it will be eminently suitable for undergraduate and graduate students interested in data privacy, as well as professionals wanting a concise overview. Vicenc Torra is Professor with the Department of Computing Science at Umea University, Umea, Sweden.
001451233 588__ $$aDescription based on print version record.
001451233 650_0 $$aData protection.
001451233 650_0 $$aData privacy.
001451233 655_0 $$aElectronic books.
001451233 77608 $$iPrint version:$$aTorra, Vicenç.$$tGuide to data privacy.$$dCham : Springer, 2022$$z9783031128363$$w(OCoLC)1346927978
001451233 830_0 $$aUndergraduate topics in computer science.
001451233 852__ $$bebk
001451233 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-12837-0$$zOnline Access$$91397441.1
001451233 909CO $$ooai:library.usi.edu:1451233$$pGLOBAL_SET
001451233 980__ $$aBIB
001451233 980__ $$aEBOOK
001451233 982__ $$aEbook
001451233 983__ $$aOnline
001451233 994__ $$a92$$bISE