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Personalized privacy protection in b...

Qu, Youyang.

Personalized privacy protection in big data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Personalized privacy protection in big databy Youyang Qu ... [et al.].
other author:	Qu, Youyang.
Published:	Singapore :Springer Singapore :2021.
Description:	xi, 139 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Big dataSecurity measures.
Online resource:	https://doi.org/10.1007/978-981-16-3750-6
ISBN:	9789811637506$q(electronic bk.)

Personalized privacy protection in big data
Personalized privacy protection in big data[electronic resource] /by Youyang Qu ... [et al.]. - Singapore :Springer Singapore :2021. - xi, 139 p. :ill., digital ;24 cm. - 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$q(electronic bk.)

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

807728
Big data
--Security measures.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.7

Personalized privacy protection in big data
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245 0 0 $a Personalized privacy protection in big data $h [electronic resource] / $c by Youyang Qu ... [et al.].
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300 $a xi, 139 p. : $b ill., digital ; $c 24 cm.
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.
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650 2 4 $a Statistics, general. $3 275684
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
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650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Coding and Information Theory. $3 273763
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