Personalized Privacy Protection in Big Data
Qu, Youyang
Personalized Privacy Protection in Big Data by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu - First edition 2021 - 1 recurso en línea (XI, 139 páginas) 36 ilustraciones, 34 ilustraciones a color - Data Analytics 2520-1859 Computer Science (SpringerNature-11645) Computer Science (R0) (SpringerNature-43710) .
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.
9789811637506
10.1007/978-981-16-3750-6 doi
Seguridad informática
QA76.9 .A25 / 2021 EB
Personalized Privacy Protection in Big Data by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu - First edition 2021 - 1 recurso en línea (XI, 139 páginas) 36 ilustraciones, 34 ilustraciones a color - Data Analytics 2520-1859 Computer Science (SpringerNature-11645) Computer Science (R0) (SpringerNature-43710) .
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.
9789811637506
10.1007/978-981-16-3750-6 doi
Seguridad informática
QA76.9 .A25 / 2021 EB