Can I read Privacy-Preserving Machine Learning on EtoBox?

Privacy-Preserving Machine Learning by Jin Li; Ping Li; Zheli Liu; Xiaofeng Chen; Tong Li is a nonfiction available to read on EtoBox.

What is Privacy-Preserving Machine Learning about?

The series aims to develop and disseminate an understanding of innovations, paradigms, techniques, and technologies in the contexts of cyber security systems and networks related research and studies. It publishes thorough and cohesive overviews of state-of-the-art topics in cyber security, as well as sophisticated techniques, original research presentations and in-depth case studies in cyber systems and networks. The series also provides a single point of coverage of advanced and timely emerging topics as well as a forum for core concepts that may not have reached a level of maturity to warrant a comprehensive textbook. It addresses security, privacy, availability, and dependability issues for cyber systems and networks, and welcomes emerging technologies, such as artificial intelligence, cloud computing, cyber physical systems, and big data analytics related to cyber security research. The mainly focuses on the following research topics:

Who reads Privacy-Preserving Machine Learning?

Author

Jin Li; Ping Li; Zheli Liu; Xiaofeng Chen; Tong Li

Publisher

Springer Singapore : Imprint: Springer

Published

2022

Language

EN

ISBN

9789811691393

Category

nonfiction

Subjects

Computer Science, Mathematics, Science