TY  - GEN
N2  - 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.
DO  - 10.1007/978-981-16-3750-6
DO  - doi
AB  - 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.
T1  - Personalized privacy protection in big data /
DA  - 2021.
CY  - Singapore :
AU  - Qu, Youyang,
AU  - Nosouhi, Mohammad Reza,
AU  - Cui, Lei,
AU  - Yu, Shui
CN  - QA76.9.A25
PB  - Springer,
PP  - Singapore :
PY  - 2021.
ID  - 1438579
KW  - Big data
KW  - Données volumineuses
SN  - 9789811637506
SN  - 9811637504
SN  - 9811637490
SN  - 9789811637490
TI  - Personalized privacy protection in big data /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3750-6
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3750-6
ER  -