張樂峰 博士
助理教授 碩導
學校郵箱：lfzhang@cityu.edu.mo
辦公電話：(853)8590 2269
辦公地址：澳門城市大學(氹仔)何鴻燊樓S501室
學歷
2023 計算機科學與技術博士, 悉尼科技大學, 澳大利亞悉尼
2019 信息安全管理碩士, 中南財經政法大學，中國湖北
2016 計算機科學與技術學士, 中南財經政法大學，中國湖北
現任
澳門城市大學數據科學學院助理教授
任教經歷
2023.02-2023.07 悉尼科技大學， course 41180 - Data Analytics in Cybersecurity
2023.02-2023.07 悉尼科技大學， course 420107 - Cybersecurity Analytics and Insight
研究方向
Differential privacy， game theory
研究及出版
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• 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).
學術獎項
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.
