Personalized Privacy Protection in Big Data (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Data Analytics
Release date:	July 2022
First published:	2021
Authors:	Youyang Qu • Mohammad Reza Nosouhi • Lei Cui • Shui Yu
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	139
Edition:	1st ed. 2021
ISBN-13:	978-981-16-3752-0
Categories:	Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Social & legal aspects of computing > Privacy & data protection Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-16-3752-0
Barcode:	9789811637520

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