Personalized Privacy Protection in Big Data

This book explores emerging threats and existing privacy protection methods, discussing the advantages and disadvantages of personalized privacy protection in various applications, such as cyber-physical systems, social networks, and location-based services.

Format: Paperback / softback
Length: 139 pages
Publication date: 26 July 2022
Publisher: Springer Verlag, Singapore

The protection of data privacy has become increasingly important in our era of big data, but research in this field is still in its early stages. Traditional protection methods may result in low data utility and unbalanced trade-offs, leading to the growth of personalized privacy protection as a rapidly expanding research topic.

In this book, the authors delve into emerging threats and existing privacy protection methods, examining both the advantages and disadvantages of personalized privacy protection. Traditional approaches such as differential privacy and cryptography are discussed using a comparative and intersectional approach, contrasting them with emerging methods like federated learning and generative adversarial nets. The advances covered in the book span various applications, including cyber-physical systems, social networks, and location-based services.

Due to its comprehensive scope, the book appeals to a wide audience, including scientists, policy-makers, researchers, and postgraduate students. It provides valuable insights into the challenges and opportunities associated with data privacy in the age of big data and serves as a valuable resource for anyone interested in this field.

Weight: 244g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811637520
Edition number: 1st ed. 2021