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Personalized Privacy Protection in Big Data (Data Analytics)Название: Personalized Privacy Protection in Big Data
Автор: Youyang Qu, Mohammad Reza Nosouhi
Издательство: Springer
Год: 2021
Страниц: 148
Язык: английский
Формат: pdf (true), epub
Размер: 16.4 MB

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.

Personalized privacy protection is a set of emerging technologies that can personalize the privacy protection based on various indexes, such as social distance in social networks and the trade-off between privacy protection and data utility. It attracts extensive interest from both academia and industry. It can be integrated with almost all the existing mainstream privacy protection frameworks, including differential privacy, clustering-based methods, and machine learning-based models, which makes it potentially applicable in many real-world scenarios.

In this book, the target is to systematically review the state-of-the-art research of personalized privacy protection and showcase the corresponding applications. This book aims to pave the way for the forthcoming researchers, engineers, and other readers to explore this under-explored domain.

This is the first book that specifically focuses on the personalized solutions of privacy protection in Big Data scenarios. Most other books either mentioned personalized privacy as a future work or barely consider it as a key component. In addition to preliminary theoretical contents and conceptual explanations, this book also simplifies the interpretative procedure by jointly presenting the several corresponding applications, which are readable to both dedicated researchers and interested readers without research background in this era.

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