Privacy Preserving Machine Learning is a comprehensive introduction to data privacy in machine learning. Based on years of DARPA-funded cybersecurity research, the book is filled with lightbulb moments that will change the way you think about algorithm design. You’ll learn how to apply privacy-enhancing techniques to common machine learning tasks, and experiment with source code fresh from the latest academic papers. Learn more about the book here: http://mng.bz/go5Z

1. Protect sensitive data
and retain ML model
accuracy
with Privacy-Preserving Machine Learning.
Take 40% off by entering slzhuang into
the discount code box at checkout at
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2. Keep sensitive user data safe and
secure, without sacrificing the
accuracy of your machine learning
models. From search histories to
medical records, many machine
learning systems are trained on
personal and sensitive data. It’s an
ongoing challenge to keep the
private details of users secure
throughout the ML process without
adversely affecting the performance
of your models.

3. Privacy Preserving Machine Learning
is a comprehensive introduction to
data privacy in machine learning.
Based on years of DARPA-funded
cybersecurity research, the book is
filled with lightbulb moments that
will change the way you think about
algorithm design. You’ll learn how to
apply privacy-enhancing techniques
to common machine learning tasks,
and experiment with source code
fresh from the latest academic
papers.

4. This book 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. 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.

5. What people are saying
about the book:
An interesting book
under a rising hot
topic: privacy. I like
the way using
examples and
figures to illustrate
concepts.
-Xiangbo Mao
Gives a deep and
thorough introduction
into preserving privacy
while using personal
data for machine
learning and data
mining.
-Harald Kuhn
A great resource
to understand
privacy
preserving ML.
-Dhivya Sivasubramanian

6. About the authors:
J. Morris Chang is a professor in the Department of Electrical Engineering of the
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.

7. Take 40% off Privacy-Preserving Machine
Learning by entering slzhuang into the
discount code box at checkout at
manning.com.
If you want to learn more about the
book, check it out on our browser-based
liveBook platform here.