Privacy-Preserving Machine Learning, Video Edition

Privacy-Preserving Machine Learning, Video Edition

by Morris Chang, Dumindu Samaraweera, Di Zhuang
Released May 2023
Publisher(s): Manning Publications
ISBN: None

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Video description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models.

In Privacy Preserving Machine Learning, you will learn:

  • Privacy considerations in machine learning
  • Differential privacy techniques for machine learning
  • Privacy-preserving synthetic data generation
  • Privacy-enhancing technologies for data mining and database applications
  • Compressive privacy for machine learning

Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.

About the Technology
Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end.

About the Book
Privacy Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter.

What's Inside
  • Differential and compressive privacy techniques
  • Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning
  • Privacy-preserving synthetic data generation
  • Enhanced privacy for data mining and database applications


About the Reader
For machine learning engineers and developers. Examples in Python and Java.

About the Authors
J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. G. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software.

Quotes
A detailed treatment of differential privacy, synthetic data generation, and privacy-preserving machine-learning techniques with relevant Python examples. Highly recommended!
- Abe Taha, Google

A wonderful synthesis of theoretical and practical. This book fills a real need.
- Stephen Oates, Allianz

The definitive source for creating privacy-respecting machine learning systems. This area in data-rich environments is so important to understand!
- Mac Chambers, Roy Hobbs Diamond Enterprises

Covers all aspects for data privacy, with good practical examples.
- Vidhya Vinay, Streamingo Solutions

Table of contents

  1. Part 1. Basics of privacy-preserving machine learning with differential privacy
  2. Chapter 1. Privacy considerations in machine learning
  3. Chapter 1. The threat of learning beyond the intended purpose
  4. Chapter 1. Threats and attacks for ML systems
  5. Chapter 1. Securing privacy while learning from data: Privacy-preserving machine learning
  6. Chapter 1. How is this book structured?
  7. Chapter 1. Summary
  8. Chapter 2. Differential privacy for machine learning
  9. Chapter 2. Mechanisms of differential privacy
  10. Chapter 2. Properties of differential privacy
  11. Chapter 2. Summary
  12. Chapter 3. Advanced concepts of differential privacy for machine learning
  13. Chapter 3. Differentially private supervised learning algorithms
  14. Chapter 3. Differentially private unsupervised learning algorithms
  15. Chapter 3. Case study: Differentially private principal component analysis
  16. Chapter 3. Summary
  17. Part 2. Local differential privacy and synthetic data generation
  18. Chapter 4. Local differential privacy for machine learning
  19. Chapter 4. The mechanisms of local differential privacy
  20. Chapter 4. Summary
  21. Chapter 5. Advanced LDP mechanisms for machine learning
  22. Chapter 5. Advanced LDP mechanisms
  23. Chapter 5. A case study implementing LDP naive Bayes classification
  24. Chapter 5. Summary
  25. Chapter 6. Privacy-preserving synthetic data generation
  26. Chapter 6. Assuring privacy via data anonymization
  27. Chapter 6. DP for privacy-preserving synthetic data generation
  28. Chapter 6. Case study on private synthetic data release via feature-level micro-aggregation
  29. Chapter 6. Summary
  30. Part 3. Building privacy-assured machine learning applications
  31. Chapter 7. Privacy-preserving data mining techniques
  32. Chapter 7. Privacy protection in data processing and mining
  33. Chapter 7.3 Protecting privacy by modifying the input
  34. Chapter 7. Protecting privacy when publishing data
  35. Chapter 7. Summary
  36. Chapter 8. Privacy-preserving data management and operations
  37. Chapter 8. Privacy protection beyond k-anonymity
  38. Chapter 8. Protecting privacy by modifying the data mining output
  39. Chapter 8. Privacy protection in data management systems
  40. Chapter 8. Summary
  41. Chapter 9. Compressive privacy for machine learning
  42. Chapter 9. The mechanisms of compressive privacy
  43. Chapter 9. Using compressive privacy for ML applications
  44. Chapter 9. Case study: Privacy-preserving PCA and DCA on horizontally partitioned data
  45. Chapter 9. Summary
  46. Chapter 10. Putting it all together: Designing a privacy-enhanced platform (DataHub)
  47. Chapter 10. Understanding the research collaboration workspace
  48. Chapter 10. Integrating privacy and security technologies into DataHub
  49. Chapter 10. Summary

Product information

  • Title: Privacy-Preserving Machine Learning, Video Edition
  • Author(s): Morris Chang, Dumindu Samaraweera, Di Zhuang
  • Release date: May 2023
  • Publisher(s): Manning Publications
  • ISBN: None