| 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 |
| Preprocess-then-NTT Technique and Its Applications to Kyber and NewHope S Zhou, H Xue, D Zhang, K Wang, X Lu, B Li, J He Information Security and Cryptology: 14th International Conference, Inscrypt …, 2019 | 37 | 2019 |
| Label-only model inversion attacks: Attack with the least information T Zhu, D Ye, S Zhou, B Liu, W Zhou IEEE Transactions on Information Forensics and Security 18, 991-1005, 2022 | 36 | 2022 |
| Fed-EINI: An efficient and interpretable inference framework for decision tree ensembles in vertical federated learning X Chen, S Zhou, B Guan, K Yang, H Fao, H Wang, Y Wang 2021 IEEE international conference on big data (big data), 1242-1248, 2021 | 24 | 2021 |
| Boosting model inversion attacks with adversarial examples S Zhou, T Zhu, D Ye, X Yu, W Zhou IEEE Transactions on Dependable and Secure Computing 21 (3), 1451-1468, 2023 | 15 | 2023 |
| Model inversion attack against transfer learning: Inverting a model without accessing it D Ye, H Chen, S Zhou, T Zhu, W Zhou, S Ji arXiv preprint arXiv:2203.06570, 2022 | 6 | 2022 |
| Label-only model inversion attack: The attack that requires the least information D Ye, T Zhu, S Zhou, B Liu, W Zhou arXiv preprint arXiv:2203.06555, 2022 | 4 | 2022 |
| Inversion-guided Defense: Detecting Model Stealing Attacks by Output Inverting S Zhou, T Zhu, D Ye, W Zhou, W Zhao IEEE Transactions on Information Forensics and Security, 2024 | 3 | 2024 |
| Defending Against Neural Network Model Inversion Attacks via Data Poisoning S Zhou, D Ye, T Zhu, W Zhou arXiv preprint arXiv:2412.07575, 2024 | | 2024 |
| Privacy Attacks and Defenses under Security Threats in Machine Learning S Zhou PQDT-Global, 2024 | | 2024 |
| SecureBP from homomorphic encryption Q Liu, X Lu, F Luo, S Zhou, J He, K Wang Secur. Commun. Networks, 2020 | | 2020 |
