04283nam 22007215 450 991062929450332120230810133407.09783031128370(electronic bk.)978303112836310.1007/978-3-031-12837-0(MiAaPQ)EBC7131945(Au-PeEL)EBL7131945(CKB)25280521400041(DE-He213)978-3-031-12837-0(PPN)266349609(EXLCZ)992528052140004120221104d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierGuide to Data Privacy Models, Technologies, Solutions /by Vicenç Torra1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (323 pages)Undergraduate Topics in Computer Science,2197-1781Print version: Torra, Vicenç Guide to Data Privacy Cham : Springer International Publishing AG,c2022 9783031128363 Includes bibliographical references and index.1. 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.Data 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.Undergraduate Topics in Computer Science,2197-1781Data protectionLaw and legislationData protectionCryptographyData encryption (Computer science)Information technologyMoral and ethical aspectsComputers and civilizationPrivacyData and Information SecurityCryptologyInformation EthicsComputers and SocietyData protectionLaw and legislation.Data protection.Cryptography.Data encryption (Computer science).Information technologyMoral and ethical aspects.Computers and civilization.Privacy.Data and Information Security.Cryptology.Information Ethics.Computers and Society.323.448005.8Torra Vicenç848974MiAaPQMiAaPQMiAaPQ9910629294503321Guide to Data Privacy2968234UNINA