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020 _a9789811917974
_9978-981-19-1797-4
024 7 _a10.1007/978-981-19-1797-4
_2doi
050 4 _aQA76.9.A25
050 4 _aJC596-596.2
072 7 _aURD
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072 7 _aCOM060040
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082 0 4 _a005.8
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082 0 4 _a323.448
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100 1 _aQu, Youyang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aPrivacy Preservation in IoT: Machine Learning Approaches
_h[electronic resource] :
_bA Comprehensive Survey and Use Cases /
_cby Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXI, 119 p. 39 illus., 36 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _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.
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.
650 0 _aData protection
_xLaw and legislation.
650 0 _aMachine learning.
650 0 _aInternet of things.
650 0 _aBig data.
650 0 _aData mining.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aPrivacy.
650 2 4 _aMachine Learning.
650 2 4 _aInternet of Things.
650 2 4 _aBig Data.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Science.
700 1 _aGao, Longxiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aYu, Shui.
_eauthor.
_0(orcid)
_10000-0003-4485-6743
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXiang, Yong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811917967
776 0 8 _iPrinted edition:
_z9789811917981
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-981-19-1797-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185485
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