Personalized Privacy Protection in Big Data / / by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu
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| Autore: |
Qu Youyang
|
| Titolo: |
Personalized Privacy Protection in Big Data / / by Youyang Qu, Mohammad Reza Nosouhi, Lei Cui, Shui Yu
|
| Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Edizione: | 1st ed. 2021. |
| Descrizione fisica: | 1 online resource (148 pages) |
| Disciplina: | 005.8 |
| Soggetto topico: | 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 | |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Personalized privacy protection in big data |
| ISBN: | 981-16-3750-4 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910495251903321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Data Analytics, . 2520-1867