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Personalized Privacy Protection in Big Data

Youyang QuMohammad Reza NosouhiLei CuiShui Yu

出版	Springer Nature, 2021-07-23
主題	Computers / Internet / Online Safety & PrivacyMathematics / Probability & Statistics / GeneralComputers / Data Science / Data AnalyticsComputers / Information TheoryComputers / Artificial Intelligence / GeneralComputers / Security / GeneralComputers / Information TechnologyComputers / Programming / AlgorithmsLanguage Arts & Disciplines / Library & Information Science / General
ISBN	98116375049789811637506
URL	http://books.google.com.hk/books?id=cfE5EAAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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.