Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases
商品資訊
ISBN13：9789811917967
出版社：Springer Nature
作者：Youyang Qu
出版日：2022/05/29
裝訂：平裝
規格：23.4cm*15.6cm*0.7cm (高/寬/厚)
商品簡介
- Chapter 1: Introduction
o Privacy research landscape
o Machine learning driven privacy preservation overview
o Contribution of this monograph
o Outline of the monograph
- Chapter 2: Current Methods of Privacy Protection in IoTs
o Cryptography based methods
o Differential privacy methods
o Anonymity-based methods
o Clustering-based methods
- Chapter 3: Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning
o Overview
o System Modelling
o Decentralized Privacy Protocols
o Blockchain-enabled Federated Learning
- Chapter 4: Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy
o Overview
o System Modelling
o Personalized Privacy
o GAN-Enhanced Differential Privacy
- Chapter 5: Hybrid Privacy Protection of IoT using Reinforcement Learning
o Overview
o System Modelling
o Hybrid Privacy
o Markov Decision Process and Reinforcement Learning
- Chapter 6: Future Directions
o Trade-off optimization
o Privacy preservation of digital twin
o Privacy-preserving federated learning
o Federated generative adversarial nets
- Chapter 7: Summary and Outlook
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