國立虎尾科技大學 |

Personalized Privacy Protection in Big Data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Personalized Privacy Protection in Big Data/ by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu.
Author:	Qu, Youyang.
other author:	Nosouhi, Mohammad Reza.
Description:	XI, 139 p. 36 illus., 34 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer security. -
Online resource:	https://doi.org/10.1007/978-981-16-3750-6
ISBN:	9789811637506

Personalized Privacy Protection in Big Data
Qu, Youyang.

Personalized Privacy Protection in Big Data[electronic resource] /by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu. - 1st ed. 2021. - XI, 139 p. 36 illus., 34 illus. in color.online resource. - Data Analytics,2520-1859. - Data Analytics,.

Chapter 1: Introduction -- Chapter 2: Current Methods of Privacy Protection -- Chapter 3: Privacy Attacks -- Chapter 4: Personalize Privacy Defense -- Chapter 5: Future Directions -- Chapter6: Summary and Outlook.

This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic. In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets. The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.

ISBN: 9789811637506

Standard No.: 10.1007/978-981-16-3750-6doiSubjects--Topical Terms:

557122
Computer security.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Personalized Privacy Protection in Big Data
LDR:02630nam a22004095i 4500 001 1049378
003 DE-He213
005 20210723205555.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811637506 $9 978-981-16-3750-6
024 7 $a 10.1007/978-981-16-3750-6 $2 doi
035 $a 978-981-16-3750-6
050 4 $a QA76.9.A25
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
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 Personalized Privacy Protection in Big Data $h [electronic resource] / $c by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XI, 139 p. 36 illus., 34 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 Data Analytics, $x 2520-1859
505 0 $a Chapter 1: Introduction -- Chapter 2: Current Methods of Privacy Protection -- Chapter 3: Privacy Attacks -- Chapter 4: Personalize Privacy Defense -- Chapter 5: Future Directions -- Chapter6: Summary and Outlook.
520 $a This book presents the data privacy protection which has been extensively applied in our current era of big data. However, research into big data privacy is still in its infancy. Given the fact that existing protection methods can result in low data utility and unbalanced trade-offs, personalized privacy protection has become a rapidly expanding research topic. In this book, the authors explore emerging threats and existing privacy protection methods, and discuss in detail both the advantages and disadvantages of personalized privacy protection. Traditional methods, such as differential privacy and cryptography, are discussed using a comparative and intersectional approach, and are contrasted with emerging methods like federated learning and generative adversarial nets. The advances discussed cover various applications, e.g. cyber-physical systems, social networks, and location-based services. Given its scope, the book is of interest to scientists, policy-makers, researchers, and postgraduates alike.
650 0 $a Computer security. $3 557122
650 0 $a Statistics . $3 1253516
650 0 $a Data mining. $3 528622
650 0 $a Data structures (Computer science). $3 680370
650 0 $a Artificial intelligence. $3 559380
650 0 $a Coding theory. $3 561460
650 0 $a Information theory. $3 595305
650 1 4 $a Privacy. $3 575491
650 2 4 $a Statistics, general. $3 671463
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Data Structures and Information Theory. $3 1211601
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Coding and Information Theory. $3 669784
700 1 $a Nosouhi, Mohammad Reza. $e author. $1 https://orcid.org/0000-0001-6959-0975 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1353485
700 1 $a Cui, Lei. $e author. $1 https://orcid.org/0000-0002-1932-1440 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1353486
700 1 $a Yu, Shui. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1021989
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811637490
776 0 8 $i Printed edition: $z 9789811637513
776 0 8 $i Printed edition: $z 9789811637520
830 0 $a Data Analytics, $x 2520-1859 $3 1280842
856 4 0 $u https://doi.org/10.1007/978-981-16-3750-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)