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Personalized privacy protection in big data [electronic resource]
- 其他作者：
- 其他題名：
- Data analytics.
- 出版： Singapore : Springer Singapore :Imprint: Springer
- 叢書名： Data analytics,
- 主題： Big data--Security measures. , Privacy. , Statistics, general. , Data Mining and Knowledge Discovery. , Data Structures and Information Theory. , Artificial Intelligence. , Coding and Information Theory.
- ISBN： 9789811637506 (electronic bk.) 、 9789811637490 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 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.
- 摘要註: 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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讀者標籤：
- 系統號： 005537698 | 機讀編目格式
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