| TextBugger: Generating Adversarial Text Against Real-world Applications J Li, S Ji, T Du, B Li, T Wang Proceedings of the Network and Distributed System Security Symposium (NDSS), 2019 | 790 | 2019 |
| Supporting anonymous location queries in mobile environments with privacygrid B Bamba, L Liu, P Pesti, T Wang International Conference on World Wide Web, 2008 | 650 | 2008 |
| Privacy-aware mobile services over road networks T Wang, L Liu Proceedings of the VLDB Endowment 2 (1), 1042-1053, 2009 | 231 | 2009 |
| Interpretable deep learning under fire X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang USENIX Security Symposium, 2020 | 208 | 2020 |
| Model-reuse attacks on deep learning systems Y Ji, X Zhang, S Ji, X Luo, T Wang ACM Conference on Computer and Communications Security, 2018 | 204 | 2018 |
| Label inference attacks against vertical federated learning C Fu, X Zhang, S Ji, J Chen, J Wu, S Guo, J Zhou, AX Liu, T Wang USENIX Security Symposium, 2022 | 200 | 2022 |
| Differentially private releasing via deep generative model (technical report) X Zhang, S Ji, T Wang ArXiv Pre-prints, 2018 | 185 | 2018 |
| Graph Backdoor Z Xi, R Pang, S Ji, T Wang USENIX Security Symposium, 2021 | 173 | 2021 |
| Deepsec: A uniform platform for security analysis of deep learning model X Ling, S Ji, J Zou, J Wang, C Wu, B Li, T Wang IEEE Symposium on Security and Privacy, 2019 | 169 | 2019 |
| Sirenattack: Generating adversarial audio for end-to-end acoustic systems T Du, S Ji, J Li, Q Gu, T Wang, R Beyah ACM ASIA Conference on Computer and Communications Security, 2020 | 147 | 2020 |
| Tokenscope: Automatically detecting inconsistent behaviors of cryptocurrency tokens in ethereum T Chen, Y Zhang, Z Li, X Luo, T Wang, R Cao, X Xiao, X Zhang ACM Conference on Computer and Communications Security, 2019 | 146 | 2019 |
| Trojaning Language Models for Fun and Profit X Zhang, Z Zhang, T Wang 6th IEEE European Symposium on Security and Privacy (EuroS&P '21), 2021 | 145 | 2021 |
| Adaptive routing for sensor networks using reinforcement learning P Wang, T Wang IEEE International Conference on Computer and Information Technology, 2006 | 133 | 2006 |
| A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models R Pang, X Zhang, S Ji, Y Vorobeychik, X Luo, T Wang ACM Conference on Computer and Communications Security, 2020 | 120 | 2020 |
| Backdoor pre-trained models can transfer to all L Shen, S Ji, X Zhang, J Li, J Chen, J Shi, C Fang, J Yin, T Wang Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 119 | 2021 |
| Unifuzz: A holistic and pragmatic metrics-driven platform for evaluating fuzzers Y Li, S Ji, Y Chen, S Liang, WH Lee, Y Chen, C Lyu, C Wu, R Beyah, ... USENIX Security Symposium, 2021 | 114 | 2021 |
| Differentially private distributed online learning C Li, P Zhou, L Xiong, Q Wang, T Wang IEEE transactions on knowledge and data engineering 30 (8), 1440-1453, 2018 | 111 | 2018 |
| Private, yet practical, multiparty deep learning X Zhang, S Ji, H Wang, T Wang IEEE International Conference on Distributed Computing Systems, 2017 | 82 | 2017 |
| Backdoor attacks against learning systems Y Ji, X Zhang, T Wang IEEE Conference on Communications and Network Security, 2017 | 81 | 2017 |
| An Invisible Black-Box Backdoor Attack Through Frequency Domain T Wang, Y Yao, F Xu, S An, H Tong, T Wang European Conference on Computer Vision (ECCV), 2022 | 73 | 2022 |
