語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Privacy Preservation in IoT: Machine Learning Approaches = A Comprehensive Survey and Use Cases /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Privacy Preservation in IoT: Machine Learning Approaches/ by Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
其他題名:
A Comprehensive Survey and Use Cases /
作者:
Qu, Youyang.
其他作者:
Xiang, Yong.
面頁冊數:
XI, 119 p. 39 illus., 36 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Data Science. -
電子資源:
https://doi.org/10.1007/978-981-19-1797-4
ISBN:
9789811917974
Privacy Preservation in IoT: Machine Learning Approaches = A Comprehensive Survey and Use Cases /
Qu, Youyang.
Privacy Preservation in IoT: Machine Learning Approaches
A Comprehensive Survey and Use Cases /[electronic resource] :by Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang. - 1st ed. 2022. - XI, 119 p. 39 illus., 36 illus. in color.online resource. - 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
Standard No.: 10.1007/978-981-19-1797-4doiSubjects--Topical Terms:
1174436
Data Science.
LC Class. No.: QA76.9.A25
Dewey Class. No.: 005.8
Privacy Preservation in IoT: Machine Learning Approaches = A Comprehensive Survey and Use Cases /
LDR
:03449nam a22004215i 4500
001
1093779
003
DE-He213
005
20220427122905.0
007
cr nn 008mamaa
008
221228s2022 si | s |||| 0|eng d
020
$a
9789811917974
$9
978-981-19-1797-4
024
7
$a
10.1007/978-981-19-1797-4
$2
doi
035
$a
978-981-19-1797-4
050
4
$a
QA76.9.A25
050
4
$a
JC596-596.2
072
7
$a
URD
$2
bicssc
072
7
$a
COM060040
$2
bisacsh
072
7
$a
URD
$2
thema
082
0 4
$a
005.8
$2
23
082
0 4
$a
323.448
$2
23
100
1
$a
Qu, Youyang.
$e
author.
$0
(orcid)0000-0002-2944-4647
$1
https://orcid.org/0000-0002-2944-4647
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1353484
245
1 0
$a
Privacy Preservation in IoT: Machine Learning Approaches
$h
[electronic resource] :
$b
A Comprehensive Survey and Use Cases /
$c
by Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang.
250
$a
1st ed. 2022.
264
1
$a
Singapore :
$b
Springer Nature Singapore :
$b
Imprint: Springer,
$c
2022.
300
$a
XI, 119 p. 39 illus., 36 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
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
2 4
$a
Data Science.
$3
1174436
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Big Data.
$3
1017136
650
2 4
$a
Internet of Things.
$3
1048478
650
2 4
$a
Machine Learning.
$3
1137723
650
1 4
$a
Privacy.
$3
575491
650
0
$a
Artificial intelligence—Data processing.
$3
1366684
650
0
$a
Data mining.
$3
528622
650
0
$a
Big data.
$3
981821
650
0
$a
Internet of things.
$3
1023130
650
0
$a
Machine learning.
$3
561253
650
0
$a
Data protection—Law and legislation.
$3
1366218
700
1
$a
Xiang, Yong.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1062830
700
1
$a
Yu, Shui.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1021989
700
1
$a
Gao, Longxiang.
$e
author.
$0
(orcid)0000-0002-3026-7537
$1
https://orcid.org/0000-0002-3026-7537
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1262517
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811917967
776
0 8
$i
Printed edition:
$z
9789811917981
830
0
$a
SpringerBriefs in Computer Science,
$x
2191-5768
$3
1255334
856
4 0
$u
https://doi.org/10.1007/978-981-19-1797-4
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入