In Privacy Preserving Machine Learning, you will learn:
- Privacy consideations 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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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.
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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