| Differentially private data publishing and analysis: A survey T Zhu, G Li, W Zhou, SY Philip IEEE Transactions on Knowledge and Data Engineering 29 (8), 1619-1638, 2017 | 355 | 2017 |
| Security and privacy in 6G networks: New areas and new challenges M Wang, T Zhu, T Zhang, J Zhang, S Yu, W Zhou Digital Communications and Networks 6 (3), 281-291, 2020 | 354 | 2020 |
| Correlated differential privacy: Hiding information in non-IID data set T Zhu, P Xiong, G Li, W Zhou IEEE Transactions on Information Forensics and Security 10 (2), 229-242, 2014 | 235 | 2014 |
| Machine Unlearning: A Survey H Xu, T Zhu, L Zhang, W Zhou, PS Yu ACM Computing Surveys 56 (1), 1-36, 2023 | 209* | 2023 |
| A blockchain-based location privacy-preserving crowdsensing system M Yang, T Zhu, K Liang, W Zhou, RH Deng Future Generation Computer Systems 94, 408-418, 2019 | 208 | 2019 |
| Local differential privacy and its applications: A comprehensive survey M Yang, T Guo, T Zhu, I Tjuawinata, J Zhao, KY Lam Computer Standards & Interfaces 89, 103827, 2024 | 207 | 2024 |
| More than privacy: Applying differential privacy in key areas of artificial intelligence T Zhu, D Ye, W Wang, W Zhou, SY Philip IEEE Transactions on Knowledge and Data Engineering 34 (6), 2824-2843, 2020 | 175 | 2020 |
| 差分隐私保护及其应用 熊平， 朱天清， 王晓峰 计算机学报 37 (1), 101-122, 2014 | 154* | 2014 |
| Location privacy and its applications: A systematic study B Liu, W Zhou, T Zhu, L Gao, Y Xiang IEEE access 6, 17606-17624, 2018 | 153 | 2018 |
| Differential privacy and applications T Zhu, G Li, W Zhou, SY Philip Springer International Publishing, 2017 | 116 | 2017 |
| Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity S Zhou, C Liu, D Ye, T Zhu, W Zhou, PS Yu ACM Computing Surveys 55 (8), 1-39, 2022 | 102 | 2022 |
| From distributed machine learning to federated learning: In the view of data privacy and security S Shen, T Zhu, D Wu, W Wang, W Zhou Concurrency and Computation: Practice and Experience 34 (16), e6002, 2022 | 102 | 2022 |
| A blockchain-based decentralized, fair and authenticated information sharing scheme in zero trust internet-of-things Y Liu, X Hao, W Ren, R Xiong, T Zhu, KKR Choo, G Min IEEE Transactions on Computers 72 (2), 501-512, 2022 | 99 | 2022 |
| Correlated differential privacy: Feature selection in machine learning T Zhang, T Zhu, P Xiong, H Huo, Z Tari, W Zhou IEEE Transactions on Industrial Informatics 16 (3), 2115-2124, 2019 | 90 | 2019 |
| An effective privacy preserving algorithm for neighborhood-based collaborative filtering T Zhu, Y Ren, W Zhou, J Rong, P Xiong Future Generation Computer Systems 36, 142-155, 2014 | 89 | 2014 |
| Resource allocation in IoT edge computing via concurrent federated reinforcement learning Z Tianqing, W Zhou, D Ye, Z Cheng, J Li IEEE Internet of Things Journal 9 (2), 1414-1426, 2021 | 85 | 2021 |
| BoSMoS: A blockchain-based status monitoring system for defending against unauthorized software updating in industrial Internet of Things S He, W Ren, T Zhu, KKR Choo IEEE Internet of Things Journal 7 (2), 948-959, 2019 | 83 | 2019 |
| Machine learning differential privacy with multifunctional aggregation in a fog computing architecture M Yang, T Zhu, B Liu, Y Xiang, W Zhou IEEE Access 6, 17119-17129, 2018 | 83 | 2018 |
| Differential privacy for neighborhood-based collaborative filtering T Zhu, G Li, Y Ren, W Zhou, P Xiong Proceedings of the 2013 IEEE/ACM international conference on advances in …, 2013 | 81 | 2013 |
| Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination T Zhang, T Zhu, J Li, M Han, W Zhou, P Yu IEEE Transactions on Knowledge and Data Engineering, 2020 | 71 | 2020 |
