| VerifyNet: Secure and verifiable federated learning G Xu, H Li, S Liu, K Yang, X Lin IEEE Transactions on Information Forensics and Security 15, 911-926, 2020 | 552 | 2020 |
| Efficient and privacy-enhanced federated learning for industrial artificial intelligence M Hao, H Li, X Luo, G Xu, H Yang, S Liu IEEE Transactions on Industrial Informatics 16 (10), 6532-6542, 2019 | 459 | 2019 |
| Enabling efficient and geometric range query with access control over encrypted spatial data G Xu, H Li, Y Dai, K Yang, X Lin IEEE Transactions on Information Forensics and Security 14 (4), 870-885, 2018 | 189 | 2018 |
| Towards efficient and privacy-preserving federated deep learning M Hao, H Li, G Xu, S Liu, H Yang ICC 2019-2019 IEEE international conference on communications (ICC), 1-6, 2019 | 173 | 2019 |
| Privacy-Enhanced Federated Learning against Poisoning Adversaries X Liu, H Li, G Xu, Z Chen, X Huang, R Lu IEEE Transactions on Information Forensics and Security, 2021 | 146 | 2021 |
| Efficient and privacy-preserving truth discovery in mobile crowd sensing systems G Xu, H Li, S Liu, M Wen, R Lu IEEE Transactions on Vehicular Technology 68 (4), 3854-3865, 2019 | 126 | 2019 |
| PTAS: Privacy-preserving thin-client authentication scheme in blockchain-based PKI W Jiang, H Li, G Xu, M Wen, G Dong, X Lin Future Generation Computer Systems 96, 185-195, 2019 | 109 | 2019 |
| Privacy-preserving Federated Deep Learning with Irregular Users G Xu, H Li, Y Zhang, S Xu, J Ning, R Deng IEEE Transactions on Dependable and Secure Computing, 2020 | 106 | 2020 |
| Data security issues in deep learning: Attacks, countermeasures, and opportunities G Xu, H Li, H Ren, K Yang, RH Deng IEEE Communications Magazine 57 (11), 116-122, 2019 | 100 | 2019 |
| Match in my way: Fine-grained bilateral access control for secure cloud-fog computing S Xu, J Ning, Y Li, Y Zhang, G Xu, X Huang, RH Deng IEEE Transactions on Dependable and Secure Computing 19 (2), 1064-1077, 2020 | 81 | 2020 |
| Achieving efficient and privacy-preserving truth discovery in crowd sensing systems G Xu, H Li, C Tan, D Liu, Y Dai, K Yang Computers & Security 69, 114-126, 2017 | 76 | 2017 |
| Adaptive privacy-preserving federated learning X Liu, H Li, G Xu, R Lu, M He Peer-to-Peer Networking and Applications, 2020 | 49 | 2020 |
| Iron: Private inference on transformers M Hao, H Li, H Chen, P Xing, G Xu, T Zhang Advances in neural information processing systems 35, 15718-15731, 2022 | 45 | 2022 |
| Towards Secure and Privacy-Preserving Distributed Deep Learning in Fog-Cloud Computing Y Li, H Li, G Xu, T Xiang, X Huang, R Lu IEEE Internet of Things Journal, 2020 | 43 | 2020 |
| Privacy-aware and resource-saving collaborative learning for healthcare in cloud computing M Hao, H Li, G Xu, Z Liu, Z Chen ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 41 | 2020 |
| Privacy-preserving efficient verifiable deep packet inspection for cloud-assisted middlebox H Ren, H Li, D Liu, G Xu, N Cheng, X Shen IEEE Transactions on Cloud Computing 10 (2), 1052-1064, 2020 | 37 | 2020 |
| Achieving privacy-preserving federated learning with irrelevant updates over e-health applications H Chen, H Li, G Xu, Y Zhang, X Luo ICC 2020-2020 IEEE international conference on communications (ICC), 1-6, 2020 | 34 | 2020 |
| Efficient, Private and Robust Federated Learning M Hao, H Li, G Xu, H Chen, T Zhang Annual Computer Security Applications Conference (ACSAC), 45-60, 2021 | 33 | 2021 |
| PADL: Privacy-aware and asynchronous deep learning for IoT applications X Liu, H Li, G Xu, S Liu, Z Liu, R Lu IEEE Internet of Things Journal 7 (8), 6955-6969, 2020 | 31 | 2020 |
| Secure and Verifiable Inference in Deep Neural Networks G Xu, H Li, H Ren, J Sun, S Xu, J Ning, H Yang, K Yang, RH Deng Annual Computer Security Applications Conference (ACSAC), 1-15, 2020 | 28 | 2020 |
Hongwei LiIEEE Fellow, University of Electronic Science and Technology of China在 uestc.edu.cn 的电子邮件经过验证
