LEADER 04283nam 22007215 450 001 9910629294503321 005 20230810133407.0 010 $a9783031128370$b(electronic bk.) 010 $z9783031128363 024 7 $a10.1007/978-3-031-12837-0 035 $a(MiAaPQ)EBC7131945 035 $a(Au-PeEL)EBL7131945 035 $a(CKB)25280521400041 035 $a(DE-He213)978-3-031-12837-0 035 $a(PPN)266349609 035 $a(EXLCZ)9925280521400041 100 $a20221104d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGuide to Data Privacy $eModels, Technologies, Solutions /$fby Vicenç Torra 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (323 pages) 225 1 $aUndergraduate Topics in Computer Science,$x2197-1781 311 08$aPrint version: Torra, Vicenç Guide to Data Privacy Cham : Springer International Publishing AG,c2022 9783031128363 320 $aIncludes bibliographical references and index. 327 $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. 330 $aData privacy technologies are essential for implementing information systems with privacy by design. Privacy technologies clearly are needed for ensuring that data does not lead to disclosure, but also that statistics or even data-driven machine learning models do not lead to disclosure. For example, can a deep-learning model be attacked to discover that sensitive data has been used for its training? This accessible textbook presents privacy models, computational definitions of privacy, and methods to implement them. Additionally, the book explains and gives plentiful examples of how to implement?among other models?differential privacy, k-anonymity, and secure multiparty computation. Topics and features: Provides integrated presentation of data privacy (including tools from statistical disclosure control, privacy-preserving data mining, and privacy for communications) Discusses privacy requirements and tools for different types of scenarios, including privacy for data, for computations, and for users Offers characterization of privacy models, comparing their differences, advantages, and disadvantages Describes some of the most relevant algorithms to implement privacy models Includes examples of data protection mechanisms This unique textbook/guide contains numerous examples and succinctly and comprehensively gathers the relevant information. As such, it will be eminently suitable for undergraduate and graduate students interested in data privacy, as well as professionals wanting a concise overview. Vicenç Torra is Professor with the Department of Computing Science at Umeå University, Umeå, Sweden. 410 0$aUndergraduate Topics in Computer Science,$x2197-1781 606 $aData protection$xLaw and legislation 606 $aData protection 606 $aCryptography 606 $aData encryption (Computer science) 606 $aInformation technology$xMoral and ethical aspects 606 $aComputers and civilization 606 $aPrivacy 606 $aData and Information Security 606 $aCryptology 606 $aInformation Ethics 606 $aComputers and Society 615 0$aData protection$xLaw and legislation. 615 0$aData protection. 615 0$aCryptography. 615 0$aData encryption (Computer science). 615 0$aInformation technology$xMoral and ethical aspects. 615 0$aComputers and civilization. 615 14$aPrivacy. 615 24$aData and Information Security. 615 24$aCryptology. 615 24$aInformation Ethics. 615 24$aComputers and Society. 676 $a323.448 676 $a005.8 700 $aTorra$b Vicenc?$0848974 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910629294503321 996 $aGuide to Data Privacy$92968234 997 $aUNINA