Security and Privacy in Web 3.0
Description
Web 3.0 is the next generation of the Internet built on decentralized technologies such as blockchain and cryptography. It is designed to address issues encountered in the previous generation of the Internet such as imbalanced distribution of interests, monopoly of platform resources, and leakage of personal privacy. This book explores the challenges and solutions related to ensuring security and privacy in the context of the evolving Web 3.0 architecture.
Web 3.0 represents the next generation of the web, characterized by decentralized networks, blockchain technology, and enhanced user control over data. As Web 3.0 evolves, the focus on addressing security and privacy concerns becomes increasingly crucial. This book provides a comprehensive understanding of the security and privacy issues specific to Web 3.0 and offers practical defense frameworks and methods to mitigate these challenges.
This book is dedicated to the specific aspects of security and privacy in Web 3.0, from introducing the architecture and addressing the inherent issues to presenting innovative methods for privacy-preserving computing, user behavior identification, and abnormal transaction detection. It is of particular interest to researchers in the field of Web 3.0, blockchain, and network security, as it summarizes the security and privacy concerns in Web 3.0 and brings a number of innovative technologies and practical solutions to protect sensitive data and maintain user privacy in Web 3.0 environments, equipping researchers with the knowledge necessary to design secure and privacy-aware applications and systems in Web 3.0.
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Meng Shen is a professor with the School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China. He received the B.Eng degree from Shandong University, Jinan, China, in 2009, and the Ph.D. degree from Tsinghua University, Beijing, China, in 2014, both in computer science. His research interests include data privacy and security, blockchain applications, and encrypted traffic classification. He has authored over 50 papers in top-level journals and conferences, such as IEEE S&P, Usenix Security, IEEE JSAC, and IEEE TIFS. He has guest edited special issues on emerging technologies for data security and privacy in IEEE Network and IEEE Internet-of-Things Journal. He received the Best Paper Award at IEEE/ACM IWQoS 2021. He was selected by the Beijing Nova Program 2020 and was the winner of the ACM SIGCOMM China Rising Star Award 2019. He is a member of the IEEE and the ACM.
Xiangyun Tang is an associate professor with the School of Information Engineering, Minzu University of China. She received the B.Eng degree in computer science from Minzu University of China, Beijing, China in 2016, and the Ph.D. degree in Cyberspace Security from Beijing Institute of Technology, Beijing, China in 2022. She has served as the guest editor for multiple Journals, and the TPC chair and the PC member for international conferences and workshops. She was a recipient of the Best Paper Award from IEEE ICBCTIS 2023, and a recipient of Outstanding Paper Award from IEEE iThings 2023. Her research interests include secure multi-party computation and machine learning security.
Wei Wang is a professor with School of Cyber Science and Engineering, Xi'an Jiaotong University. He is also an adjunct Professor with school of computer science and technology, Beijing Jiaotong University. He received the Ph.D. degree from Xi'an Jiaotong University, in 2006. He was a Post-Doctoral Researcher with the University of Trento, Italy, from 2005 to 2006. He was a Post-Doctoral Researcher with TELECOM Bretagne and with INRIA, France, from 2007 to 2008. He was also a European ERCIM Fellow with the Norwegian University of Science and Technology (NTNU), Norway, and with the Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, from 2009 to 2011. He was a faculty member with Beijing Jiaotong University, from 2011 to 2024. His recent research interests lie in data security and privacy-preserving computation. He has authored or co-authored over 100 peer-reviewed articles in various journals and international conferences, including IEEE TDSC, IEEE TIFS, IEEE TSE, USENIX Security, ACM CCS, AAAI, Ubicomp, IEEE INFOCOM. He received the ACM CCS 2023 Distinguished Paper Award. He is an Elsevier ``highly cited Chinese Researchers''. He is an Associate Editor for IEEE Transactions on Dependable and Secure Computing, and an Editorial Board Member of Computers & Security and of Frontiers of Computer Science. He is a vice chair of ACM SIGSAC China.
Liehuang Zhu is a professor with the School of Cyberspace Science and Technology, Beijing Institute of Technology. He received the Ph.D. degree in computer science from Beijing Institute of Technology, Beijing, China, in 2004. He has published more than 100 peer-reviewed journal or conference papers, including 10 more IEEE/ACM Transactions papers. His research interests include security protocol analysis and design, wireless sensor networks, and cloud computing. Prof. Zhu has been granted a number of IEEE Best Paper Awards, including IWQoS 17' and TrustCom 18'.
Content
Chapter 1. Introduction of Web 3.0.- Chapter 2. Security and Privacy Defense Framework for Web 3.0.- Chapter 3. Verifiable Privacy-preserving Federated Learning in Web 3.0.- Chapter 4. Membership Privacy Protection for Federated Learning in Web 3.0.- Chapter 5. Secure Mobile Device Authentication in Web 3.0.- Chapter 6. User Behavior Identification via Traffic Analysis in Web 3.0.- Chapter 7. Malicious Transaction Detection in Web 3.0.- Chapter 8. Malicious Transaction Deanonymity in Web 3.0.- Chapter 9. Conclusion and Future Direction.
