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001438579 001__ 1438579
001438579 003__ OCoLC
001438579 005__ 20230309004312.0
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001438579 008__ 210731s2021\\\\si\a\\\\o\\\\\000\0\eng\d
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001438579 020__ $$a9789811637506$$q(electronic bk.)
001438579 020__ $$a9811637504$$q(electronic bk.)
001438579 020__ $$a9811637490
001438579 020__ $$a9789811637490
001438579 0247_ $$a10.1007/978-981-16-3750-6$$2doi
001438579 035__ $$aSP(OCoLC)1262373219
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001438579 049__ $$aISEA
001438579 050_4 $$aQA76.9.A25
001438579 08204 $$a005.7$$223
001438579 1001_ $$aQu, Youyang,$$eauthor
001438579 24510 $$aPersonalized privacy protection in big data /$$cYouyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu.
001438579 260__ $$aSingapore :$$bSpringer,$$c2021.
001438579 300__ $$a1 online resource (xi, 139 pages) :$$billustrations
001438579 336__ $$atext$$btxt$$2rdacontent
001438579 337__ $$acomputer$$bc$$2rdamedia
001438579 338__ $$aonline resource$$bcr$$2rdacarrier
001438579 347__ $$atext file
001438579 347__ $$bPDF
001438579 4901_ $$aData analytics
001438579 5050_ $$aChapter 1: Introduction -- Chapter 2: Current Methods of Privacy Protection -- Chapter 3: Privacy Attacks -- Chapter 4: Personalize Privacy Defense -- Chapter 5: Future Directions -- Chapter6: Summary and Outlook.
001438579 506__ $$aAccess limited to authorized users.
001438579 520__ $$aThis book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic. In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets. The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.
001438579 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 3, 2021).
001438579 650_0 $$aBig data$$xSecurity measures.
001438579 650_6 $$aDonnées volumineuses$$xSécurité$$xMesures.
001438579 655_0 $$aElectronic books.
001438579 7001_ $$aNosouhi, Mohammad Reza,$$eauthor
001438579 7001_ $$aCui, Lei,$$eauthor
001438579 7001_ $$aYu, Shui$$c(Computer scientist),$$eauthor.
001438579 77608 $$iPrint version:$$aQu, Youyang.$$tPersonalized Privacy Protection in Big Data.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811637490
001438579 830_0 $$aData analytics.
001438579 852__ $$bebk
001438579 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3750-6$$zOnline Access$$91397441.1
001438579 909CO $$ooai:library.usi.edu:1438579$$pGLOBAL_SET
001438579 980__ $$aBIB
001438579 980__ $$aEBOOK
001438579 982__ $$aEbook
001438579 983__ $$aOnline
001438579 994__ $$a92$$bISE