Description
| Product ID: | 9789811637490 |
| Product Form: | Hardback |
| Country of Manufacture: | GB |
| Series: | Data Analytics |
| Title: | Personalized Privacy Protection in Big Data |
| Authors: | Author: Lei Cui, Youyang Qu, Shui Yu, Mohammad Reza Nosouhi |
| Page Count: | 139 |
| Subjects: | Information theory, Information theory, Coding theory and cryptology, Databases, Data mining, Computer security, Privacy and data protection, Expert systems / knowledge-based systems, Coding theory & cryptology, Databases, Data mining, Computer security, Privacy & data protection, Expert systems / knowledge-based systems |
| Description: | Select Guide Rating 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. 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. |
| Imprint Name: | Springer Verlag, Singapore |
| Publisher Name: | Springer Verlag, Singapore |
| Country of Publication: | GB |
| Publishing Date: | 2021-07-25 |
