Wang Cong is a Professor and the Associate Head in the Department of Computer Science, College of Computing, City University of Hong Kong. He earned his bachelor’s and master’s degrees both from Wuhan University, China, and his Ph.D. from the Illinois Institute of Technology, USA. His research work spans across data security and privacy, AI systems and security, and blockchain and decentralized applications. He has made prolific contributions to these fields, witnessed by 30,000+ citations on Google Scholar and multiple best paper awards, including the 2020 IEEE INFOCOM Test of Time Paper Award. Prof. Wang holds the title of IEEE Fellow, HK RGC Research Fellow, and is a Founding Member of the Young Academy of Sciences of Hong Kong. At CityU of Hong Kong, Prof. Wang has received the Outstanding Research Award (2019), the Outstanding Supervisor Award (2017), and two President’s Awards (2016, 2019). He currently serves as the Editor-in-Chief for the IEEE Transactions on Dependable and Secure Computing, a leading security journal within the IEEE Computer Society. Additionally, he is a senior scientist at The Laboratory for AI-Powered Financial Technologies Limited (AIFT) and has been appointed by the Hong Kong Monetary Authority as a member of the Central Bank Digital Currency (CBDC) Expert Group.
In this data-driven era, the paramount concern for data security has never been more critical. Traditional security measures, which protect data at rest or in transit, are inadequate against the evolving cyber threats where attackers might gain administrative privileges. Prof. Wang has significantly advanced this field by developing innovative algorithms and systems for protecting data in use, including new designs for encrypted databases, privacy-preserving data analytics platforms, and encrypted deep packet inspection systems, all underpinned by solid theoretical frameworks and practical performance metrics. As artificial intelligence reshapes the landscape of financial technology, Prof. Wang has broadened his research to AI security. His work systematically identifies and mitigates potential model hijacking vulnerabilities across the AI lifecycle, from data preprocessing to model deployment, covering both digital realms and physical contexts. His innovative designs have significantly enhanced the integrity and robustness of AI operations in FinTech, which is crucial for the sector’s operational continuity. These innovations collectively pave the way for a future where data ownership and security are integral to system design, offering a robust foundation for the next generation of smart FinTech applications.