Privacy-Preserving Machine Learning - Softcover

Über die Autorin bzw. den Autor

J. Morris Chang is a professor in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He received his PhDfrom North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He hasled 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. Heconducted 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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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 datastorage, 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 tocreate machine learning systems that preserve user privacy without sacrificing data quality and model performance.