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Personalized privacy protection in b...

Qu, Youyang.

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Personalized privacy protection in big data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Personalized privacy protection in big data/ by Youyang Qu ... [et al.].
其他作者:	Qu, Youyang.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xi, 139 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Big data - Security measures. -
電子資源:	https://doi.org/10.1007/978-981-16-3750-6
ISBN:	9789811637506

Personalized privacy protection in big data
Personalized privacy protection in big data[electronic resource] /by Youyang Qu ... [et al.]. - Singapore :Springer Singapore :2021. - xi, 139 p. :ill., digital ;24 cm. - Data analytics,2520-1859. - Data analytics..

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.

ISBN: 9789811637506

Standard No.: 10.1007/978-981-16-3750-6doiSubjects--Topical Terms:

3310623
Big data
--Security measures.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.7

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505 0 $a 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.
520 $a 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.
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