Privacy-Preserving Machine Learning
by J. Morris Chang (Author), Di Zhuang (Author), G. Dumindu Samaraweera (Author)
Publisher Finelybook 出版社：Manning (February 21, 2023)
pages 页数：343 pages
Complex privacy-enhancing technologies are demystified through real-world use cases for facial recognition, cloud data storage, and more.
Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You’ll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning.
Complex privacy-enhancing technologies are demystified through real-world use cases for facial recognition, cloud data storage, and more. Alongside skills for technical implementation, you’ll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you’re done, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Privacy-Preserving Machine Learning MEAP V08
Chapter 1: Privacy considerations in machine learning
Chapter 2: Differential privacy for machine learning
Chapter 3: Advanced concepts of differential privacy for machine learning
Chapter 4: Local differential privacy for machine learning
Chapter 5: Advanced mechanisms of local differential privacy for machine learning
Chapter 6: Privacy-preserving synthetic data generation
Chapter 7: Privacy-preserving data mining techni ques
Chapter 8: Privacy-preserving data management and operations
Chapter 9: Compressive privacy for machine learning
Chapter 10: Putting it all together: designing a privacy-enhanced platform for research data protection and sharing (DataHub)
Appendix A: More details about Differential Privacy
MEAP Version 8