國立高雄大學圖資館 |

登入

回首頁

Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Privacy preservation in IoTby Youyang Qu ... [et al.].
其他題名:	machine learning approaches : a comprehensive survey and use cases /
其他作者:	Qu, Youyang.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xi, 119 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Internet of thingsSecurity measures.
電子資源:	https://doi.org/10.1007/978-981-19-1797-4
ISBN:	9789811917974$q(electronic bk.)

Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /
Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /[electronic resource] :by Youyang Qu ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xi, 119 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Chapter 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.

This 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.

ISBN: 9789811917974$q(electronic bk.)

Standard No.: 10.1007/978-981-19-1797-4doiSubjects--Topical Terms:

770009
Internet of things
--Security measures.

LC Class. No.: TK5105.8857

Dewey Class. No.: 004.678

Privacy preservation in IoTmachine learning approaches : a comprehensive survey and use cases /
LDR:03070nmm a2200337 a 4500 001 620635
003 DE-He213
005 20220427122905.0
006 m d
007 cr nn 008maaau
008 221121s2022 si s 0 eng d
020 $a 9789811917974$q(electronic bk.)
020 $a 9789811917967$q(paper)
024 7 $a 10.1007/978-981-19-1797-4 $2 doi
035 $a 978-981-19-1797-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.8857
072 7 $a URD $2 bicssc
072 7 $a COM060040 $2 bisacsh
072 7 $a URD $2 thema
082 0 4 $a 004.678 $2 23
090 $a TK5105.8857 $b .P961 2022
245 0 0 $a Privacy preservation in IoT $h [electronic resource] : $b machine learning approaches : a comprehensive survey and use cases / $c by Youyang Qu ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xi, 119 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5776
505 0 $a Chapter 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 $a This 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 $a Internet of things $x Security measures. $3 770009
650 0 $a Data privacy. $3 919820
650 1 4 $a Privacy. $3 268519
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Internet of Things. $3 913131
650 2 4 $a Big Data. $3 760530
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Science. $3 913495
700 1 $a Qu, Youyang. $3 901356
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u https://doi.org/10.1007/978-981-19-1797-4
950 $a Computer Science (SpringerNature-11645)