This book provides a thorough overview of the evolution of
privacy-preserving machine learning schemes over the last ten years,
after discussing the importance of privacy-preserving techniques. In
response to the diversity of Internet services, data services based on
machine learning are now available for various applications, including
risk assessment and image recognition. In light of open access to
datasets and not fully trusted environments, machine learning-based
applications face enormous security and privacy risks. In turn, it
presents studies conducted to address privacy issues and a series of
proposed solutions for ensuring privacy protection in machine learning
tasks involving multiple parties. In closing, the book reviews
state-of-the-art privacy-preserving techniques and examines the security
threats they face.