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Keep sensitive user data safe and secure, without sacrificing the accuracy of your machine learning models.An interesting and well structured book about an emerging discipline that will certainly keep growing in importance.
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 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.
About the Technology
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.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 authors
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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