001446321 000__ 04029cam\a2200565Ii\4500
001446321 001__ 1446321
001446321 003__ OCoLC
001446321 005__ 20230310003951.0
001446321 006__ m\\\\\o\\d\\\\\\\\
001446321 007__ cr\un\nnnunnun
001446321 008__ 220430s2022\\\\si\a\\\\ob\\\\000\0\eng\d
001446321 019__ $$a1313480013$$a1314608970
001446321 020__ $$a9789811917974$$q(electronic bk.)
001446321 020__ $$a9811917973$$q(electronic bk.)
001446321 020__ $$z9789811917967
001446321 020__ $$z9811917965
001446321 0247_ $$a10.1007/978-981-19-1797-4$$2doi
001446321 035__ $$aSP(OCoLC)1313387323
001446321 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCQ
001446321 049__ $$aISEA
001446321 050_4 $$aTK5105.8857
001446321 08204 $$a004.67/8$$223/eng/20220509
001446321 1001_ $$aQu, Youyang,$$eauthor.
001446321 24510 $$aPrivacy preservation in IoT :$$bmachine learning approaches : a comprehensive survey and use cases /$$cYouyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
001446321 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001446321 264_4 $$c©2022
001446321 300__ $$a1 online resource :$$billustrations (chiefly color).
001446321 336__ $$atext$$btxt$$2rdacontent
001446321 337__ $$acomputer$$bc$$2rdamedia
001446321 338__ $$aonline resource$$bcr$$2rdacarrier
001446321 4901_ $$aSpringerBriefs in computer science,$$x2191-5776
001446321 504__ $$aIncludes bibliographical references.
001446321 5050_ $$aChapter 1 Introduction -- Chapter 2 Current Methods of Privacy Protection in IoTs -- Chapter 3 Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning -- Chapter 4 Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy -- Chapter 5 Hybrid Privacy Protection of IoT using Reinforcement Learning -- Chapter 6 Future Directions -- Chapter 7 Summary and Outlook.
001446321 506__ $$aAccess limited to authorized users.
001446321 520__ $$aThis book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.
001446321 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 9, 2022).
001446321 650_0 $$aInternet of things$$xSecurity measures.
001446321 650_0 $$aData privacy.
001446321 655_0 $$aElectronic books.
001446321 7001_ $$aGao, Longxiang,$$eauthor.
001446321 7001_ $$aYu, Shui$$c(Computer scientist),$$eauthor.
001446321 7001_ $$aXiang, Yong,$$eauthor.
001446321 77608 $$iPrint version: $$z9811917965$$z9789811917967$$w(OCoLC)1302741250
001446321 830_0 $$aSpringerBriefs in computer science.$$x2191-5776
001446321 852__ $$bebk
001446321 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-1797-4$$zOnline Access$$91397441.1
001446321 909CO $$ooai:library.usi.edu:1446321$$pGLOBAL_SET
001446321 980__ $$aBIB
001446321 980__ $$aEBOOK
001446321 982__ $$aEbook
001446321 983__ $$aOnline
001446321 994__ $$a92$$bISE