Personalized Privacy Protection in Big Data [electronic resource] /
Material type: TextSeries: Data AnalyticsPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2021Edition: 1st ed. 2021Description: XI, 139 p. 36 illus., 34 illus. in color. online resourceContent type:- text
- computer
- online resource
- 9789811637506
- Data protection -- Law and legislation
- Quantitative research
- Data mining
- Artificial intelligence -- Data processing
- Coding theory
- Information theory
- Computer security
- Privacy
- Data Analysis and Big Data
- Data Mining and Knowledge Discovery
- Data Science
- Coding and Information Theory
- Principles and Models of Security
- 005.8 23
- 323.448 23
- QA76.9.A25
- JC596-596.2
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 bookis of interest to scientists, policy-makers, researchers, and postgraduates alike.
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