| A robust game-theoretical federated learning framework with joint differential privacy L Zhang, T Zhu, P Xiong, W Zhou, SY Philip IEEE Transactions on Knowledge and Data Engineering 35 (4), 3333-3346, 2022 | 72 | 2022 |
| Fedrecovery: Differentially private machine unlearning for federated learning frameworks L Zhang, T Zhu, H Zhang, P Xiong, W Zhou IEEE Transactions on Information Forensics and Security 18, 4732-4746, 2023 | 63 | 2023 |
| More than privacy: Adopting differential privacy in game-theoretic mechanism design L Zhang, T Zhu, P Xiong, W Zhou, PS Yu ACM Computing Surveys (CSUR) 54 (7), 1-37, 2021 | 39 | 2021 |
| Reward-based spatial crowdsourcing with differential privacy preservation P Xiong, L Zhang, T Zhu Enterprise Information Systems 11 (10), 1500-1517, 2017 | 35 | 2017 |
| A game-theoretic method for defending against advanced persistent threats in cyber systems L Zhang, T Zhu, FK Hussain, D Ye, W Zhou IEEE Transactions on Information Forensics and Security 18, 1349-1364, 2022 | 26 | 2022 |
| Private collaborative filtering under untrusted recommender server P Xiong, L Zhang, T Zhu, G Li, W Zhou Future generation computer systems 109, 511-520, 2020 | 22 | 2020 |
| A differentially private method for reward-based spatial crowdsourcing L Zhang, X Lu, P Xiong, T Zhu International Conference on Applications and Techniques in Information …, 2015 | 16 | 2015 |
| A game-theoretic federated learning framework for data quality improvement L Zhang, T Zhu, P Xiong, W Zhou, SY Philip IEEE Transactions on Knowledge and Data Engineering 35 (11), 10952-10966, 2022 | 15 | 2022 |
| Optimizing rewards allocation for privacy-preserving spatial crowdsourcing P Xiong, D Zhu, L Zhang, W Ren, T Zhu Computer Communications 146, 85-94, 2019 | 13 | 2019 |
| PriFace: a privacy-preserving face recognition framework under untrusted server S Zhao, L Zhang, P Xiong Journal of Ambient Intelligence and Humanized Computing 14 (3), 2967-2979, 2023 | 9 | 2023 |
| Semantic analysis in location privacy preserving P Xiong, L Zhang, T Zhu Concurrency and Computation: Practice and Experience 28 (6), 1884-1899, 2016 | 5 | 2016 |
| Really unlearned? verifying machine unlearning via influential sample pairs H Xu, T Zhu, L Zhang, W Zhou arXiv preprint arXiv:2406.10953, 2024 | 4 | 2024 |
| Update selective parameters: Federated machine unlearning based on model explanation H Xu, T Zhu, L Zhang, W Zhou, SY Philip IEEE Transactions on Big Data, 2024 | 3 | 2024 |
| The Price of Unlearning: Identifying Unlearning Risk in Edge Computing L Zhang, T Zhu, P Xiong, W Zhou ACM Transactions on Multimedia Computing, Communications and Applications, 2024 | 3 | 2024 |
| A differentially private method for crowdsourcing data submission L Zhang, P Xiong, W Ren, T Zhu Concurrency and Computation: Practice and Experience 31 (19), e5100, 2019 | 3 | 2019 |
| Federated TrustChain: Blockchain-enhanced LLM training and unlearning X Zuo, M Wang, T Zhu, L Zhang, D Ye, S Yu, W Zhou arXiv preprint arXiv:2406.04076, 2024 | 2 | 2024 |
| Federated learning with blockchain-enhanced machine unlearning: A trustworthy approach X Zuo, M Wang, T Zhu, L Zhang, S Yu, W Zhou arXiv preprint arXiv:2405.20776, 2024 | 2 | 2024 |
| Privacy preservation auction in a dynamic social network X Hu, Z Jin, L Zhang, A Zhou, D Ye Concurrency and Computation: Practice and Experience 34 (16), e6058, 2022 | 2 | 2022 |
| A survey on location privacy preserving techniques X Lu, L Zhang, P Xiong Computer Science and Application 6 (06), 2016 | 2 | 2016 |
| Toward Efficient Target-Level Machine Unlearning Based on Essential Graph H Xu, T Zhu, L Zhang, W Zhou, W Zhao IEEE Transactions on Neural Networks and Learning Systems, 2024 | 1 | 2024 |
