Personalized Privacy Protection in Big Data

Qu, Y; Nosouhi, MR; Cui, L; Yu, S

Personalized Privacy Protection in Big Data

Qu, Y Nosouhi, MR Cui, L Yu, S

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Publisher:: Springer
Publication Type:: Book
Citation:: 2021, pp. 1-139
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Qu, Y
dc.contributor.author	Nosouhi, MR
dc.contributor.author	Cui, L
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-10-31T05:05:33Z
dc.date.available	2021-10-31T05:05:33Z
dc.date.issued	2021
dc.identifier.citation	2021, pp. 1-139
dc.identifier.isbn	9789811637490
dc.identifier.uri	http://hdl.handle.net/10453/151283
dc.description.abstract	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.
dc.language	en
dc.publisher	Springer
dc.relation.isbasedon	10.1007/978-981-16-3750-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Personalized Privacy Protection in Big Data
dc.type	Book
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-10-31T05:05:32Z
pubs.place-of-publication	Singapore
pubs.publication-status	Published
dc.location	Singapore

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/151283