Guangzhou University | China
Dr. Shaowei Wang is currently an Associate Professor at the School of Artificial Intelligence, Guangzhou University. He earned his Ph.D. from the University of Science and Technology of China (USTC) in 2019. Prior to his academic appointment, he worked as an Applied Researcher at Tencent Technology (Shenzhen), where he contributed to industry-grade privacy-preserving solutions. His research primarily focuses on privacy-preserving computing, federated learning, and AI security, with a strong emphasis on differential privacy techniques and secure data sharing protocols. Dr. Wang has an impressive scholarly record, having authored or co-authored over 40 peer-reviewed papers, with 15 publications as the first or corresponding author in top-tier venues such as USENIX Security, IEEE S&P, INFOCOM, and ICDE. As of September 2025, his work has garnered 1,106 citations, achieving an h-index of 16 (Google Scholar indexed), reflecting the impact and relevance of his contributions to the field. He has been the Principal Investigator (PI) for six research projects, including three funded by the National Natural Science Foundation of China, and a key researcher in five national-level and regional-level projects. Notable ongoing research includes work on shuffled differential privacy, privacy attacks on pre-trained models, and secure digital identity protocols. His academic excellence and innovation have earned him multiple accolades, including an Honorable Mention Award at USENIX Security, a Top 3% Paper Award at ICASSP, and the First Prize in Natural Sciences from the Guangdong Artificial Intelligence Association. Dr. Wang remains committed to advancing the frontiers of privacy-preserving AI through impactful research, interdisciplinary collaboration, and high-quality publications in the global research community.
Featured Publications
Wang, S., Huang, L., Nie, Y., Zhang, X., Wang, P., Xu, H., & Yang, W. (2019). Local differential private data aggregation for discrete distribution estimation. IEEE Transactions on Parallel and Distributed Systems, 30(9), 2046–2059.
Xin, B., Yang, W., Geng, Y., Chen, S., Wang, S., & Huang, L. (2020). Private FL-GAN: Differential privacy synthetic data generation based on federated learning. In ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. [pages not provided]). IEEE.
Shen, Y., Huang, L., Li, L., Lu, X., Wang, S., & Yang, W. (2015). Towards preserving worker location privacy in spatial crowdsourcing. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE.
Nie, Y., Yang, W., Huang, L., Xie, X., Zhao, Z., & Wang, S. (2018). A utility-optimized framework for personalized private histogram estimation. IEEE Transactions on Knowledge and Data Engineering, 31(4), 655–669.
Wang, S., Huang, L., Nie, Y., Wang, P., Xu, H., & Yang, W. (2018). PrivSet: Set-valued data analyses with local differential privacy. In IEEE INFOCOM 2018 – IEEE Conference on Computer Communications (pp. 1088–1096). IEEE.
Yang, G., Wang, S., & Wang, H. (2021). Federated learning with personalized local differential privacy. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. [pages not provided]). IEEE.
Xin, B., Geng, Y., Hu, T., Chen, S., Yang, W., Wang, S., & Huang, L. (2022). Federated synthetic data generation with differential privacy. Neurocomputing, 468, 1–10.