Privacy-Preserving Machine Learning, J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera, more

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 Author

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.

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.

Table of Contents

PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY

1 Privacy considerations in machine learning

2 Differential privacy for machine learning

3 Advanced concepts of differential privacy for machine learning

PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION

4 Local differential privacy for machine learning

5 Advanced LDP mechanisms for machine learning

6 Privacy-preserving synthetic data generation

PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS

7 Privacy-preserving data mining techniques

8 Privacy-preserving data management and operations

9 Compressive privacy for machine learning

10 Putting it all together: Designing a privacy-enhanced platform (DataHub)

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