Dr. Lefeng Zhang
Assistant Professor Master Supervisor
Email：lfzhang@cityu.edu.mo
Tel：(853)8590 2269
Office address: Room S501, Stanley Ho Building, City University of Macau (Taipa)
Educational qualifications
2023 Doctor of Philosophy in Computer Science and Technology, University of Technology Sydney, Sydney, Australia
2019 Master of Information Security Management, Zhongnan University of Economics and Law, Hubei, China
2016 Bachelor of Computer Science and Technology, Zhongnan University of Economics and Law, Hubei, China
Incumbent
Assistant Professor, Faculty of Data Science, City University of Macau
Teaching experience
2023.02-2023.07 University of Technology, Sydney, course 41180 - Data Analytics in Cybersecurity
2023.02-2023.07 University of Technology Sydney, course 420107 - Cybersecurity Analytics and Insight
Research direction
Differential privacy, Game theory
Research and publishing
• L. Zhang, T. Zhu, P. Xiong, W. Zhou, and Philip S. Yu. More than Privacy: Adopting Differential Privacy in Game theoretic Mechanism Design. ACM Comput. Surv. 54, 7, 2021. (IF= 14.324).
• L. Zhang, T. Zhu, F. K. Hussain, D. Ye and W. Zhou. A Game Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems, in IEEE Transactions on Information Forensics and Security, vol.18, pp.1349 -1364, 2023, (IF=7.231, CCF-A).
• L. Zhang, T. Zhu, P. Xiong, W. Zhou and P. S. Yu. A Robust Game Theoretical Federated Learning Framework with Joint Differential Privacy, in IEEE Transactions on Knowledge and Data Engineering, vol.35, no.4, pp .3333-3346, 2023, (IF=9.235, CCF-A).
• L. Zhang, T. Zhu, H. Zhang, P Xiong, and W. Zhou. FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks, in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4732-4746 , 2023, doi: 10.1109/TIFS.2023.3297905, (IF=7.231, CCF-A).
• L. Zhang, T. Zhu, P. Xiong, W. Zhou and P. S. Yu. A Game Theoretic Federated Learning Framework for Data Quality Improvement, in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2022.3230959, (IF =9.235, CCF-A).
• L. Zhang, P. Xiong, W. Ren, T. Zhu. A differentially private method for crowdsourcing data submissions, Concurrency Computat Pract Exper. 2019; 31:e5100. (IF=1.831, CCF-C).
• L. Zhang, G. Song, D. Zhu, W. Ren, P. Xiong. Location privacy preservation through kernel transformation, Concurrency and Computation: Practice and Experience. 34. 10.1002/cpe.6014. (IF=1.831, CCF -C).
• L. Zhang, P. Xiong, T. Zhu. A differentially private method for crowdsourcing data submissions. Proc of Australasian workshop on machine learning for cybersecurity - PAKDD workshop. 2018: pages 142-148. Best Paper Award.
• L. Zhang, X. Lu, P. Xiong, T. Zhu. A Differentially Private Method for Reward Based Spatial Crowdsourcing. Applications and Techniques in Information Security. Springer, 2015: Pages 153-164.
• L. Zhang, P. Xiong, T. Zhu. Location Privacy Preserving for Semantic Aware Applications. Applications and Techniques in Information Security. Springer, 2014: 135-146. Best Student Paper Award.
• P. Xiong, L. Zhang, T. Zhu, G. Li, W. Zhou. Private collaborative filtering under untrusted recommender server. Future Generation Computer Systems, Volume 109, ages 511-520, 2020, (CCF-C, IF =7.5).
• S. Zhao, L. Zhang, P. Xiong. PriFace: a privacy preserving face recognition framework under untrusted server. Journal of Ambient Intelligence and Humanized Computing, 14, pp.2967-2979 (2023), (IF=3.662).
• P. Xiong, L. Zhang, T. Zhu. Reward based spatial crowdsourcing with differential privacy preservation. Enterprise Information Systems, 2017, 11(10): pp.1500-1517.
• P. Xiong, L. Zhang, T. Zhu. Semantic analysis in location privacy preserving. Concurrency and Computation: Practice and Experience, 2016, 28(6): 1884-1899.
• H. Xu, T. Zhu, L. Zhang, W. Zhou, and Philip S. Yu. 2023. Machine Unlearning: A Survey. ACM Comput. Surv. 56, 1, Article 9 (January 2024), 36 pages. https://doi.org/10.1145/3603620, (IF=14.324).
• P. Xiong, D. Zhu, L. Zhang, W. Ren, T. Zhu. Optimizing rewards allocation for privacy preserving spatial crowdsourcing, Computer Communications, Volume 146, Pages 85-94, (IF=1.831, CCF-C) .
• X. Hu, Z. Jin, L. Zhang, A. Zhou, D. Ye. Privacy preservation auction in a dynamic social network. Concurrency Computat Pract Exper. 2022; 34:e6058, (IF=1.831, CCF-C).
Academic Awards
Proc of Australasian workshop on machine learning for cybersecurity - PAKDD workshop. 2018. Best Paper Award.
Applications and Techniques in Information Security. Best Student Paper Award.
