唐朋，博士，副教授，山东大学未来计划学者。中国中文信息学会大数据安全与隐私计算专委会委员，蚂蚁密算科技“隐语学术委员会”委员。博士毕业于北京邮电大学，师从苏森教授。

研究方向聚焦于数据安全与隐私保护和可信人工智能领域，旨在构建从数据源头到模型应用的全链路安全架构。近年来，在CCS、ICML、ICDE、EDBT、TDSC、TKDE 等数据安全与人工智能领域高水平国际会议和期刊发表论文20余篇。主持省级以上科研项目3项，参与2项。

研究方向：

l 方向一：数据安全与隐私保护

本方向聚焦于数据在全生命周期内的隐私保护与安全保障，为构建可信数据空间提供支撑。核心研究内容包括：数据流通中的敏感信息保护、隐私强度与数据效用的均衡机制，以及隐私计算协议的鲁棒性。

l 方向二：可信人工智能

本方向致力于确保AI模型（尤其是大模型）在应用生命周期内的可靠性与安全性。核心研究内容包括：隐私保护的图神经网络学习、大语言模型中的隐私保护检索增强生成（RAG）、隐私保护的图像数据合成技术。

代表性成果：

[1]      Pei Zhan, Peng Tang*, Yangzhuo Li, Puwen Wei, Shanqing Guo. Poisoning Attacks to Local Differential Privacy for Ranking Estimation (CCS 2025, CCF-A)，2025.

[2]      Longzhu He, Chaozhuo Li, Peng Tang, and Sen Su*. Going Deeper into Locally Differentially Private Graph Neural Networks. (ICML 2025, CCF-A), 2025.

[3]      Longzhu He, Peng Tang, Yuanhe Zhang, Pengpeng Zhou, and Sen Su*. Mitigating Privacy Risks in Retrieval-Augmented Generation (RAG) via Locally Private Entity Perturbation. (IP&M 2025, 中科院1区top), 2025.

[4]   Xinglin Du, Peng Tang*, Rui Chen, Ning Wang, Chengyu Hu, Shanqing Guo. Query Rewriting-Based View Generation for Efficient Multi-Relation Multi-Query with Differential Privacy. 27th International Conference on Extending Database Technology (EDBT 2025, CCF-B), 576-588, 2025.

[5]   Yukun Yan, Peng Tang, Rui Chen*, Qilong Han*, Ruochen Du. DPC: Filtering Out Patch-Based Poisoned Samples with Differential Privacy. (ESORICS 2024, CCF-B), 289-309, 2024.

[6]     Peng Tang, Rui Chen*, Sen Su, Shanqing Guo*, Lei Ju and Gaoyuan Liu. Multi-Party Sequential Data Publishing Under Differential Privacy. IEEE Transactions on Knowledge and Data Engineering ( TKDE 2023, CCF-A), 35(9): 9562-9577, 2023.

[7]     Ning Wang, Yaohua Wang, Zhigang Wang, Jie Nie, Zhiqiang Wei, Peng Tang, Yu Gu and Ge Yu. PrivNUD: Effective Range Query Processing under Local Differential Privacy. The 39th IEEE International Conference on Data Engineering (ICDE 2023, CCF-A), 2660-2672, 2023.

[8]   Peng Tang, Rui Chen, Chongshi Jin, Gaoyuan Liu  and Shanqing Guo. Marginal Release under Multi-Party Personalized Differential Privacy.  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (PKDD 2023, CCF-B), 2022.

[9]   Gaoyuan Liu, Peng Tang*, Chengyu Hu, Chongshi Jin  and Shanqing Guo. Multi-Dimensional Data Publishing With Local Differential Privacy.  24th International Conference on Extending Database Technology (EDBT 2022, CCF-B), 2022.

[10]     Peng Tang, Rui Chen, Shanqing Guo, Lei Ju  and Gaoyuan Liu. Differentially Private Publication of Multi-Party Sequential Data. 2021 IEEE 37th International Conference on Data Engineering (ICDE 2021, CCF-A), 2021.

[11]      Peng Tang, Xiang Cheng, Sen Su, Rui Chen  and Huaxi Shao. Differentially Private Publication of Vertically Partitioned Data.  IEEE Transactions on Dependable and Secure Computing (TDSC 2021, CCF-A), 2021.

[12]      Xiang Cheng, Peng Tang*, Sen Su, Rui Chen, Zequn Wu  and Binyuan Zhu. Multi-Party High-Dimensional Data Publishing under Differential Privacy.  IEEE Transactions on Knowledge and Data Engineering (TKDE 2020, CCF-A), 2020.

[13]      Sen Su, Peng Tang, Xiang Cheng, Rui Chen  and Zequn Wu. Differentially private multi-party high-dimensional data publishing.  IEEE 32nd International Conference on Data Engineering (ICDE 2016, CCF-A), 2016.