Privacy-Preserving Computing in Practice

Erscheinungsjahr 2027
ISBN: 978-981-9253-98-2
Verlag: Springer

Buch, Englisch, 256 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Digital Privacy and Security

This book systematically summarizes the knowledge in the field of privacy-preserving computing, reorganizes the knowledge in this field from the author's perspective, and forms a system. With the privacy-preserving computing framework as the core, it systematically explains the relevant key technologies and practical application cases. The main contents include the following aspects: Introduction, including background and concepts, related technologies, and the development history, technical characteristics and application scenarios of privacy-preserving computing; related basic knowledge, including the mathematical foundation of cryptography, an overview of security models, and secure multi-party computing technology; an overview of the privacy-preserving computing framework, including the existing secret sharing computing framework and the homomorphic encryption computing framework; privacy-preserving computing primitives, including the design of privacy-preserving basic operations; practical application cases of the secure multi-party computing framework and its improved framework.

This book can be used as a textbook for doctoral students, master students, and senior undergraduates in cyberspace security-related majors, and can also be used as a reference book for scientific researchers engaged in cyberspace security research and engineering and technical personnel engaged in network security system development.

Zielgruppe

Research

Autoren/Hrsg.

Liu, Ximeng

Xiong, Jinbo

Weitere Mitwirkende

Yu, Huili

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit

Inhaltsverzeichnis

Chapter 1 Introduction.- Chapter 2 Fundamentals.- Chapter 3 Overview of Privacy-Preserving Computing Frameworks.- Chapter 4 Privacy-Preserving Computation Primitives.- Chapter 5 Exploration of an SMC Framework for Molecular Property Prediction Models.-Chapter 6 Improvements to the SMC Framework for Neural Network Training.- Chapter 7 Conclusion and Future Perspectives.

Über Autor(innen)

Ximeng Liu received the B.Sc. degree in electronic engineering from Xidian University, Xi’an, China, in 2010, and the Ph.D. degree in cryptography from Xidian University, China, in 2015. He is currently Deputy Chief Engineer at Chongqing Changan Automobile Co., Ltd., where he is affiliated with the State Key Laboratory of Intelligent Vehicle Safety Technology. He was formerly a Full Professor with the College of Mathematics and Computer Science, Fuzhou University. He was also a Research Fellow with the School of Information Systems, Singapore Management University, Singapore. He has published more than 100 papers on the topics of cloud security, big data security, and applied cryptography, including papers in the IEEE Transactions on Computers, IEEE Transactions on Dependable and Secure Computing, and the IEEE Internet of Things Journal. His research interests include cloud security, privacy-preserving computation, and cryptographic protocols for data integrity. He is a member of ACM and CCF. He was awarded the Minjiang Scholars Distinguished Professor, Qishan Scholars in Fuzhou University, and ACM SIGSAC China Rising Star Award in 2018. He has served as a program committee member for several conferences, such as the 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2017 IEEE Global Communications Conference, and 2016 IEEE Global Communications Conference. He also served as the Lead Guest Editor for Wireless Communications and Mobile Computing.

Jinbo Xiong received the Ph.D. degree in Computer System Architecture from Xidian University, China, in 2013. He is currently the full Professor and Ph.D. supervisor with the Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China. He has authored over 100 publications, 3 books and authorized 10 invention patents. His research interests include secure deep learning, data security, privacy protection, and Internet of Things. He is a fellow of EAI, senior member of IEEE and CCF, and member of ACM.

Yu Huili received the Ph.D. degree in vehicle engineering from Chongqing University, China. She is currently serving as Deputy General Manager at the Changan Forward Technology Research Institute, Chongqing Changan Automobile Co., Ltd., where she leads cutting-edge R&D in intelligent automotive technologies. Concurrently, she is also affiliated with the State Key Laboratory of Intelligent Vehicle Safety Technology, driving innovation in AI-driven safety systems. Her contributions include establishing industry-leading frameworks for AI-based risk assessment and developing next-generation cybersecurity solutions for autonomous vehicles. She has also played a pivotal role in fostering collaborations with global research institutions and tech partners, accelerating the commercialization of intelligent mobility solutions. Her work has directly contributed to Changan’s leadership in AI safety, aligning with ISO 26262 and UN-R155 cybersecurity standards. She has also led efforts in developing industry benchmarks, authoring 9 national and international standards (e.g., ISO/PAS 8800) and securing over 150 patents in automotive intelligence and safety.