王建华

职位：讲师

学历：博士研究生

学科：网络空间安全

研究领域或方向：联邦学习、人工智能安全

邮箱：wangjianhua02@tyut.edu.cn

职称：中级

个人简介
主要成果

2024.7-至今：太原理工大学讲师
2020.9-2024.6：北京交通大学计算机与信息技术学院网络空间安全博士
2017.9-2020.6：太原理工大学信息与计算机学院软件工程硕士
2013.9-2017.6：太原理工大学软件学院软件工程学士
主要从事网络空间安全领域研究工作，代表性成果是面向资源受限设备提出通信高效和隐私保护并重的联邦学习方法，解决了传统联邦学习中隐私泄露、通信瓶颈、和节点公平等挑战。IEEE TKDE、 IoTJ、 Ad Hoc Network等SCI期刊审稿人，CWSN 2024组织委员会委员。
[1] Wang J, Chang X, Mišić J, et al. CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning[J]. IEEE Transactions on Network and Service Management, 2024. (中科院2区)
[2] Wang J, Chang X, Mišić J, Mišić VB, et al. PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning [J]. IEEE Internet of Things Journal, 2024. (中科院1区TOP)
[3] Wang J, Chang X, Mišić J, Mišić VB, et al. PASS: A Parameter Audit-Based Secure and Fair Federated Learning Scheme Against Free-Rider Attack [J]. IEEE Internet of Things Journal, vol. 11, no. 1, pp. 1374-1384, 1 Jan.1, 2024. (中科院1区TOP)
[4] Wang J, Lei X, Liang M, et al. Towards Well-trained Model Robustness in Federated Learning: An Adversarial-Example-Generation-Efficiency Perspective [C]. IEEE International Conference on Communications (ICC), 2024. (CCF C会议)
[5] Wang J, Chang X, Rodrìguez R J, et al. Assessing anonymous and selfish free-rider attacks in federated learning[C]// IEEE Symposium on Computers and Communications (ISCC). IEEE, 2022: 1-6. (CCF C会议)
[6] Wang J, Chang X, Wang Y, et al. LSGAN-AT: enhancing malware detector robustness against adversarial examples[J]. Cybersecurity, 2021, 4(1): 1-15. (中科院4区)
[7] Wang J, Chang X, Mišić J, et al. Mal-LSGAN: An Effective Adversarial Malware Example Generation Model[C]// IEEE Global Communications Conference (GlobeCom). IEEE, 2021: 1-6. (CCF C会议)
[8] Wang Y, Chang X, Zhu H, Wang J, et al. Towards Secure Runtime Customizable Trusted Execution Environment on FPGA-SoC [J]. IEEE Transactions on Computers, 2024. (中科院2区，CCF A期刊)
[9] Gong Y, Chang X, Mišić J, Mišić VB, Wang J, et al. Practical Solutions in Fully Homomorphic Encryption - A Survey Analyzing Existing Acceleration Methods [J]. Cybersecurity, 2023. (中科院4区)
[10] Wang W, Wang J, et al. Exploring best-matched embedding model and classifier for charging-pile fault diagnosis[J]. Cybersecurity, 2023, 6(1): 1-13. (中科院4区)
[11] Wang Y, Liu J, Chang X, RJ Rodríguez, Wang J. DI-AA: An interpretable white-box attack for fooling deep neural networks[J]. Information Sciences, 2022, 610: 14–32. (中科院1区)
[12] Wang Y, Liu J, Chang X, Wang J, et al. AB-FGSM: AdaBelief optimizer and FGSM-based approach to generate adversarial examples [J]. Journal of Information Security and Applications, 2022, 68: 103227. (中科院3区)
[13] Yao Y, Chang X, Wang J, et al. LPC: A lightweight pseudonym changing scheme with robust forward and backward secrecy for V2X [J]. Ad Hoc Networks, 2021, 123: 102695. (中科院3区)
[14] Yao Y, Zhao Z, Chang X, Wang J. A Novel Privacy-Preserving Neural Network Computing Approach for E-Health Information System[C]// IEEE International Conference on Communications (ICC). IEEE, 2021: 1-6. (CCF C会议)
专利：
[1] 常晓林, 纪健全, 姚英英, 王建华. 面向MEC环境的基于OAuth2.0的单点登录机制, 2021.11.23, CN112822675B (已授权)
[2] 常晓林, 毛敬凯, 黎琳, 范俊超, 朱颢然, 巩艳伟, 王建华. 一种可信机密虚拟机系统的实现方法, 2023.11.24, CN117113332A (审中)
[3] 常晓林, 王燕玲, 王建华, 等. 一种面向ZYNQ SoC的FPGA可信执行环境构建方法, 2023.09.19, CN116776323A (审中)
[4] 常晓林, 王建华, 姚英英, 等. 一种抗隐私数据重构的通信高效联邦学习方法 (提交)
科研项目：
主持： 2022-2024年研究生创新项目资助
[1] 面向联邦学习搭便车攻击的评估及其防御策略研究，基本科研业务费研究生创新项目， 2022-2024
参与：
[2] 大规模联盟链共识算法效能分析及优化,国家自然科学基金面上,2023-2026。
[3] 面向多层域轨道交通“四网融合”的数据治理和可信交互关键技术研究（2）,铁路总公司（原铁道部）, 2021-2023
[6] 动静协同的恶意代码智能分析方法研究, 国家自然科学基金“联合基金项目”,2019.01-2021.12
[4] 航天信息隐私计算产品算法组件项目（子包-安全多方计算算法组件开发）, 横向, 2023.11-2024.8
[5] 规模化电动汽车与电网互动及充电安全防护技术研究, 国网, 横向, 2020.9-2022-12