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Privacy-Preserving Machine Learning

J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera
  • MEAP began December 2020
  • Publication in Fall 2021 (estimated)
  • ISBN 9781617298042
  • 300 pages (estimated)
  • printed in black & white
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An interesting and well structured book about an emerging discipline that will certainly keep growing in importance.

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

In Privacy Preserving Machine Learning, you will learn:

  • Differential privacy techniques and their application in supervised learning
  • Privacy for frequency or mean estimation, Naive Bayes classifier, and deep learning
  • Designing and applying compressive privacy for machine learning
  • Privacy-preserving synthetic data generation approaches
  • Privacy-enhancing technologies for data mining and database applications

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 and seniorities will benefit from incorporating these privacy-preserving practices into their model development.

about the technology

Large-scale scandals such as the Facebook Cambridge Analytica data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.

about the book

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.

about the reader

For machine learning engineers, and developers building around machine learning. Examples in Python and Java.

about the author

J. Morris Chang is a professor in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He received his PhD from North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He has led a DARPA project under the Brandeis Program, focusing on privacy-preserving computation over the internet for three years.

Di Zhuang received his BSc degree in computer science and information security from Nankai University, Tianjin, China. He is currently a PhD candidate in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He conducted privacy-preserving machine learning research under the DARPA Brandeis Program from 2015 to 2018.

G. Dumindu Samaraweera received his BSc degree in computer systems and networking from Curtin University, Australia, and a MSc in enterprise application development degree from Sheffield Hallam University, UK. He is currently reading for his PhD in electrical engineering at University of South Florida, Tampa.

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Gives a deep and thorough introduction into preserving privacy while using personal data for machine learning and data mining.

Harald Kuhn

Makes for a great introduction to privacy-preserving techniques which make full use of machine learning.

Aditya Kaushik

An interesting book under a rising hot topic: privacy. I like the way using examples and figures to illustrate concepts.

Xiangbo Mao

The only book, that I am aware of, which goes into depths of data privacy while making sure it doesn't get boring for readers.

Vishwesh Ravi Shrimali

An interesting book under a rising hot topic: privacy.

Xiangbo Mao

A great resource to understand privacy preserving ML.

Dhivya Sivasubramanian

A great book to getting a deep theoretical overview of the landscape of privacy preserving approaches while also getting hands-on pragmatic experience.

Stephen Oates
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