This book constitutes the refereed proceedings of the 2nd EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2019, held in Tianjin, China, in April 2019. The 62 full papers were selected from 112 submissions and are grouped into topics on privacy and security analysis, Internet of Things and cloud computing, system building, scheme, model and application for data, mechanism and method in new computing.

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.

This book constitutes the refereed proceedings of the 2nd EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2019, held in Tianjin, China, in April 2019. The 62 full papers were selected from 112 submissions and are grouped into topics on privacy and security analysis, Internet of Things and cloud computing, system building, scheme, model and application for data, mechanism and method in new computing.

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.