Privacy Preservation in IoT: Machine Learning Approaches: A Comprehensive Survey and Use Cases
Qu, Youyang, Gao, Longxiang, Yu, Shui
- 出版商: Springer
- 出版日期: 2022-04-28
- 售價: $2,610
- 貴賓價: 9.5 折 $2,480
- 語言: 英文
- 頁數: 132
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9811917965
- ISBN-13: 9789811917967
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相關分類:
Machine Learning、物聯網 IoT
海外代購書籍(需單獨結帳)
相關主題
商品描述
- 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
