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008			221112s2022 si | s |||| 0|eng d
020			_a9789811917974
024	7		_a10.1007/978-981-19-1797-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.8857 _b2022 EB
100	1		_aQu, Youyang _eautor _0(orcid)0000-0002-2944-4647 _1https://orcid.org/0000-0002-2944-4647 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681116
245	1	0	_aPrivacy Preservation in IoT: Machine Learning Approaches : _ba Comprehensive Survey and Use Cases _cby Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XI, 119 páginas) _b39 ilustraciones, 36 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Computer_2022
650		7	_2embne _9483083 _aInternet de los objetos
650		7	_2embne _9147793 _aProtección de datos
700	1		_aGao, Longxiang _eautor _0(orcid)0000-0002-3026-7537 _1https://orcid.org/0000-0002-3026-7537 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678515
700	1		_aYu, Shui _eautor _0(orcid)0000-0003-4485-6743 _1https://orcid.org/0000-0003-4485-6743 _4aut _4http://id.loc.gov/vocabulary/relators/aut _998282
700	1		_aXiang, Yong _eautor _0(orcid)0000-0001-5252-0831 _1https://orcid.org/0000-0001-5252-0831 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9670284
776	0	8	_iPrinted edition: _z9789811917967
776	0	8	_iPrinted edition: _z9789811917981
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-1797-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI