CityU

Professor Tianqing Zhu Associate Dean


Dr. Tianqing Zhu 

Professor Doctoral Supervisor/Master Supervisor 

Associate Dean (Research), Faculty of Data Science 

E-mail: tqzhu@cityu.edu.mo 

Contact number Contact: +853-85902275 

 

Educational qualifications 

2014 Doctor of Philosophy in Computer Science, Deakin University, Australia 

2004 Master in Automation Chemistry, Wuhan University, China 

2000 Bachelor of Applied Chemistry, Wuhan University, China 

 

 

Incumbent 

Associate Dean, Faculty of Data Science, City University of Macau 

 

Professor, Faculty of Data Science, City University of Macau, PhD Supervisor 

 

Courses taught 

 

Parallel Computing, Big Data and Applications 

 

Research direction 

 

Artificial Intelligence Security, Privacy Protection, Cyberspace Security 

 

Research and publishing 

 

Books 

 

Zhu, T., Zhou, W., Li, G., & Yu, P. (2017). Differential privacy and applications. In Advances in information security. Springer. 

 

Liu, B., Zhou, W., Zhu, T., Xiang, Y., & Wang, K. (2018). Location privacy in mobile applications. In Advances in information security. Springer. 

 

Refereed Journal Articles (Selected) 

 

 

  1. Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2023). Machine unlearning: A survey. ACM Computing Surveys, 56(1), 1–36.
  2. Hu, X., Zhu, T., Zhai, X., Zhou, W., & Zhao, W. (2023). Stabilization of Switched Logical Networks: Asynchronous Switching Control. IEEE Transactions on Knowledge and Data Engineering, 35(4), 4137–4150.
  3.  
  4. Zhou, S., Zhu, T., Ye, D., Yu, X., & Zhou, W. (2023). Boosting Model Inversion Attacks with Adversarial Examples. IEEE Transactions on Dependable and Secure Computing.
  5. Chen, Huiqiang, Zhu, T., Zhang, T., Zhou, W., & Yu, P. S. (2023). Privacy and Fairness in Federated Learning: on the Perspective of Trade-off. ACM Computing Surveys.
  6. Zhang, Lefeng, Zhu, T., Zhang, H., Xiong, P., & Zhou, W. (2023). Fedrecovery: Differentially private machine unlearning for federated learning frameworks. IEEE Transactions on Information Forensics and Security.
  7. Sun, H., Zhu, T., Li, J., Ji, S., & Zhou, W. (2023). Attribute-Based Membership Inference Attacks and Defenses on GANs. IEEE Transactions on Dependable and Secure Computing.
  8. Zhu, C., Cheng, Z., Ye, D., Hussain, F. K., Zhu, T., & Zhou, W. (2023). Time-driven and Privacy-preserving Navigation Model for Vehicle-to-vehicle Communication Systems. IEEE Transactions on Vehicular Technology.
  9. Liu, C., Chen, H., Zhu, T., Zhang, J., & Zhou, W. (2023). Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack. IEEE Transactions on Dependable and Secure Computing.
  10. Zhang, Lefeng, Zhu, T., Xiong, P., Zhou, W., & Philip, S. Y. (2022). A robust game-theoretical federated learning framework with joint differential privacy. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3333–3346.
  11. Liu, C., Zhu, T., Zhang, J., & Zhou, W. (2022). Privacy intelligence: A survey on image privacy in online social networks. ACM Computing Surveys, 55(8), 1–35.
  12. Ye, D., Zhu, T., Zhu, C., Zhou, W., & Philip, S. Y. (2022). Model-based self-advising for multi-agent learning. IEEE Transactions on Neural Networks and Learning Systems.
  13. Zhang, G., Liu, B., Zhu, T., Ding, M., & Zhou, W. (2022). Label-only membership inference attacks and defenses in semantic segmentation models. IEEE Transactions on Dependable and Secure Computing, 20(2), 1435–1449.
  14. Ye, D., Shen, S., Zhu, T., Liu, B., & Zhou, W. (2022). One parameter defense—defending against data inference attacks via differential privacy. IEEE Transactions on Information Forensics and Security, 17, 1466–1480.
  15. Hu, X., Zhu, T., Zhai, X., Wang, H., Zhou, W., & Zhao, W. (2022). Privacy Data Diffusion Modeling and Preserving in Online Social Network. IEEE Transactions on Knowledge and Data Engineering.
  16. Zhou, S., Liu, C., Ye, D., Zhu, T., Zhou, W., & Yu, P. S. (2022). Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity. ACM Computing Surveys, 55(8), 1–39.
  17. Yang, M., Tjuawinata, I., Lam, K.-Y., Zhu, T., & Zhao, J. (2022). Differentially Private Distributed Frequency Estimation. IEEE Transactions on Dependable and Secure Computing.
  18. Zhang, L., Zhu, T., Hussain, F. K., Ye, D., & Zhou, W. (2022). Defend to defeat: Limiting information leakage in defending against advanced persistent threats. IEEE Transactions on Information Forensics and Security, 1–1.
  19. Zhang, Lefeng, Zhu, T., Xiong, P., Zhou, W., & Philip, S. Y. (2022). A Game-theoretic Federated Learning Framework for Data Quality Improvement. IEEE Transactions on Knowledge and Data Engineering.
  20. Liu, Y., Hao, X., Ren, W., Xiong, R., Zhu, T., Choo, K.-K. R., & Min, G. (2022). A blockchain-based decentralized, fair and authenticated information sharing scheme in zero trust internet-of-things. IEEE Transactions on Computers, 72(2), 501–512.
  21. Zhang, Lefeng, Zhu, T., Hussain, F. K., Ye, D., & Zhou, W. (2022). A Game-Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems. IEEE Transactions on Information Forensics and Security, 18, 1349–1364.
  22. Liao, T., Lei, Z., Zhu, T., Zeng, S., Li, Y., & Yuan, C. (2021). Deep metric learning for k nearest neighbor classification. IEEE Transactions on Knowledge and Data Engineering, 35(1), 264–275.
  23. Zhang, Lefeng, Zhu, T., Xiong, P., Zhou, W., & Yu, P. S. (2021). More than privacy: Adopting differential privacy in game-theoretic mechanism design. ACM Computing Surveys (CSUR), 54(7), 1–37.
  24. Zhang, T., Zhu, T., Gao, K., Zhou, W., & Philip, S. Y. (2021). Balancing learning model privacy, fairness, and accuracy with early stopping criteria. IEEE Transactions on Neural Networks and Learning Systems.
  25. Hu, X., Zhu, T., Zhai, X., Zhou, W., & Zhao, W. (2021). Privacy data propagation and preservation in social media: A real-world case study. IEEE Transactions on Knowledge and Data Engineering.
  26. Wang, Z., Zhao, J., Hu, J., Zhu, T., Wang, Q., Ren, J., & Li, C. (2020). Towards personalized task-oriented worker recruitment in mobile crowdsensing. IEEE Transactions on Mobile Computing, 20(5), 2080–2093.
  27. Chivukula, A. S., Yang, X., Liu, W., Zhu, T., & Zhou, W. (2020). Game theoretical adversarial deep learning with variational adversaries. IEEE Transactions on Knowledge and Data Engineering, 33(11), 3568–3581.
  28. Zhang, T., Zhu, T., Li, J., Han, M., Zhou, W., & Yu, P. (2020). Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination. IEEE Transactions on Knowledge and Data Engineering.
  29. Ye, D., Zhu, T., Shen, S., & Zhou, W. (2020). A differentially private game theoretic approach for deceiving cyber adversaries. IEEE Transactions on Information Forensics and Security, 16, 569–584.
  30. Ye, D., Zhu, T., Shen, S., Zhou, W., & Philip, S. Y. (2020). Differentially private multi-agent planning for logistic-like problems. IEEE Transactions on Dependable and Secure Computing, 19(2), 1212–1226.
  31. Ye, D., Zhu, T., Zhou, W., & Philip, S. Y. (2019). Differentially private malicious agent avoidance in multiagent advising learning. IEEE Transactions on Cybernetics, 50(10), 4214–4227.
  32. Zhang, T., Zhu, T., Xiong, P., Huo, H., Tari, Z., & Zhou, W. (2019). Correlated differential privacy: Feature selection in machine learning. IEEE Transactions on Industrial Informatics, 16(3), 2115–2124.
  33. Xiao, R., Ren, W., Zhu, T., & Choo, K.-K. R. (2019). A mixing scheme using a decentralized signature protocol for privacy protection in bitcoin blockchain. IEEE Transactions on Dependable and Secure Computing, 18(4), 1793–1803.