[READ PDF] Kindle Privacy-Preserving Machine Learning (Ebook pdf)

Privacy-Preserving Machine Learning
[READ PDF] Kindle Privacy-Preserving Machine Learning (Ebook pdf)
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[READ PDF] Kindle Privacy-Preserving Machine Learning (Ebook pdf)

Description
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 learningDifferential privacy techniques for machine learningPrivacypreserving
synthetic data generationPrivacy-enhancing technologies for data mining and database
applicationsCompressive 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats
from Manning Publications. 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 techniquesPrivacy for frequency or
mean estimation, naive Bayes classifier, and deep learningPrivacy-preserving synthetic data
generationEnhanced 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)