| Distributed additive encryption and quantization for privacy preserving federated deep learning H Zhu, R Wang, Y Jin, K Liang, J Ning Neurocomputing 463, 309-327, 2021 | 62 | 2021 |
| PIVODL: Privacy-preserving vertical federated learning over distributed labels H Zhu, R Wang, Y Jin, K Liang IEEE Transactions on Artificial Intelligence 4 (5), 988-1001, 2021 | 35 | 2021 |
| More is better (mostly): On the backdoor attacks in federated graph neural networks J Xu, R Wang, S Koffas, K Liang, S Picek Proceedings of the 38th Annual Computer Security Applications Conference …, 2022 | 29 | 2022 |
| Feverless: Fast and secure vertical federated learning based on xgboost for decentralized labels R Wang, O Ersoy, H Zhu, Y Jin, K Liang IEEE Transactions on Big Data, 2022 | 24 | 2022 |
| Your smart contracts are not secure: Investigating arbitrageurs and oracle manipulators in ethereum K Tjiam, R Wang, H Chen, K Liang Proceedings of the 3rd Workshop on Cyber-Security Arms Race, 25-35, 2021 | 23 | 2021 |
| AN-GCN: an anonymous graph convolutional network against edge-perturbing attacks A Liu, B Li, T Li, P Zhou, R Wang IEEE transactions on neural networks and learning systems 35 (1), 88-102, 2022 | 12 | 2022 |
| Federated synthetic data generation with stronger security guarantees AR Ghavamipour, F Turkmen, R Wang, K Liang Proceedings of the 28th ACM Symposium on Access Control Models and …, 2023 | 10 | 2023 |
| FLVoogd: Robust and privacy preserving federated learning T Yuhang, W Rui, Q Yanqi, P Emmanouil, L Kaitai Asian Conference on Machine Learning, 1022-1037, 2023 | 7 | 2023 |
| BRIEF but powerful: Byzantine-robust and privacy-preserving federated learning via model segmentation and secure clustering R Wang, X Wang, H Chen, S Picek, Z Liu, K Liang arXiv preprint arXiv:2208.10161, 2022 | 7 | 2022 |
| Effect of homomorphic encryption on the performance of training federated learning generative adversarial networks I Pejic, R Wang, K Liang arXiv preprint arXiv:2207.00263, 2022 | 7 | 2022 |
| Feature engineering framework based on secure multi-party computation in federated learning L Sun, R Du, D He, S Zhu, R Wang, S Chan 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th …, 2021 | 6 | 2021 |
| Multi-flgans: multi-distributed adversarial networks for non-iid distribution A Amalan, R Wang, Y Qiao, E Panaousis, K Liang arXiv preprint arXiv:2206.12178, 2022 | 5 | 2022 |
| MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering R Wang, X Wang, H Chen, J Decouchant, S Picek, N Laoutaris, K Liang Proceedings of the ACM on Measurement and Analysis of Computing Systems 8 (3 …, 2024 | 2 | 2024 |
| An overview of hybrid approaches in Horizontal Federated Learning E Filip, K LIANG, RUI WANG TU Delft Electrical Engineering, 2021 | 1 | 2021 |
| LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm D Liu, Y Qiao, R Wang, K Liang, G Smaragdakis arXiv preprint arXiv:2411.19075, 2024 | | 2024 |
| Stealthy Backdoor Attack against Federated Learning through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage Y Qiao, D Liu, R Wang, K Liang IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2024 | | 2024 |
| Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm Y Qiao, D Liu, R Wang, K Liang arXiv preprint arXiv:2402.15653, 2024 | | 2024 |
| MUDGUARD R Wang, X Wang, H Chen, J Decouchant, S Picek, N Laoutaris, K Liang | | 2024 |
| Secure and resilient federated learning R Wang | | 2024 |
| FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning Y Qiao, D Liu, C Chen, R Wang, K Liang arXiv preprint arXiv:2309.00127, 2023 | | 2023 |
Dr. Kaitai LiangCybersecurity Group, EEMCS, TU DELFTПідтверджена електронна адреса в tudelft.nl