Personalized privacy-preserving task allocation for mobile crowdsensing Z Wang, J Hu, R Lv, J Wei, Q Wang, D Yang, H Qi IEEE Transactions on Mobile Computing 18 (6), 1330-1341, 2018 | 264 | 2018 |
Towards privacy-preserving incentive for mobile crowdsensing under an untrusted platform Z Wang, J Li, J Hu, J Ren, Z Li, Y Li IEEE INFOCOM 2019-IEEE Conference on Computer Communications, 2053-2061, 2019 | 83 | 2019 |
Towards personalized task-oriented worker recruitment in mobile crowdsensing Z Wang, J Zhao, J Hu, T Zhu, Q Wang, J Ren, C Li IEEE Transactions on Mobile Computing 20 (5), 2080-2093, 2020 | 70 | 2020 |
Threats to training: A survey of poisoning attacks and defenses on machine learning systems Z Wang, J Ma, X Wang, J Hu, Z Qin, K Ren ACM Computing Surveys 55 (7), 1-36, 2022 | 65 | 2022 |
Attrleaks on the edge: Exploiting information leakage from privacy-preserving co-inference Z Wang, K Liu, J Hu, J Ren, H Guo, W Yuan Chinese Journal of Electronics 32 (1), 1-12, 2023 | 64 | 2023 |
When mobile crowdsensing meets privacy Z Wang, X Pang, J Hu, W Liu, Q Wang, Y Li, H Chen IEEE Communications Magazine 57 (9), 72-78, 2019 | 61 | 2019 |
Towards privacy-driven truthful incentives for mobile crowdsensing under untrusted platform Z Wang, J Li, J Hu, J Ren, Q Wang, Z Li, Y Li IEEE Transactions on Mobile Computing 22 (2), 1198-1212, 2021 | 53 | 2021 |
Pay on-demand: Dynamic incentive and task selection for location-dependent mobile crowdsensing systems Z Wang, J Hu, J Zhao, D Yang, H Chen, Q Wang 2018 IEEE 38th International Conference on Distributed Computing Systems …, 2018 | 47 | 2018 |
Heterogeneous incentive mechanism for time-sensitive and location-dependent crowdsensing networks with random arrivals Z Wang, R Tan, J Hu, J Zhao, Q Wang, F Xia, X Niu Computer networks 131, 96-109, 2018 | 46 | 2018 |
Task-bundling-based incentive for location-dependent mobile crowdsourcing Z Wang, J Hu, Q Wang, R Lv, J Wei, H Chen, X Niu IEEE Communications Magazine 57 (2), 54-59, 2019 | 44 | 2019 |
Privacy-preserving task allocation for edge computing-based mobile crowdsensing X Ding, R Lv, X Pang, J Hu, Z Wang, X Yang, X Li Computers & Electrical Engineering 97, 107528, 2022 | 42 | 2022 |
Towards demand-driven dynamic incentive for mobile crowdsensing systems J Hu, Z Wang, J Wei, R Lv, J Zhao, Q Wang, H Chen, D Yang IEEE Transactions on Wireless Communications 19 (7), 4907-4918, 2020 | 33 | 2020 |
Shield against gradient leakage attacks: Adaptive privacy-preserving federated learning J Hu, Z Wang, Y Shen, B Lin, P Sun, X Pang, J Liu, K Ren IEEE/ACM Transactions on Networking 32 (2), 1407-1422, 2023 | 26 | 2023 |
Location privacy-aware task offloading in mobile edge computing Z Wang, Y Sun, D Liu, J Hu, X Pang, Y Hu, K Ren IEEE Transactions on Mobile Computing 23 (3), 2269-2283, 2023 | 25 | 2023 |
Privacy-preserving adversarial facial features Z Wang, H Wang, S Jin, W Zhang, J Hu, Y Wang, P Sun, W Yuan, K Liu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 20 | 2023 |
Towards efficient asynchronous federated learning in heterogeneous edge environments Y Zhou, X Pang, Z Wang, J Hu, P Sun, K Ren IEEE INFOCOM 2024-IEEE conference on computer communications, 2448-2457, 2024 | 11 | 2024 |
Does differential privacy really protect federated learning from gradient leakage attacks? J Hu, J Du, Z Wang, X Pang, Y Zhou, P Sun, K Ren IEEE Transactions on Mobile Computing, 2024 | 6 | 2024 |
Label-free poisoning attack against deep unsupervised domain adaptation Z Wang, W Liu, J Hu, H Guo, Z Qin, J Liu, K Ren IEEE Transactions on Dependable and Secure Computing, 2023 | 6 | 2023 |
Textual unlearning gives a false sense of unlearning J Du, Z Wang, J Zhang, X Pang, J Hu, K Ren arXiv preprint arXiv:2406.13348, 2024 | 5 | 2024 |
SoK: On Gradient Leakage in Federated Learning J Du, J Hu, Z Wang, P Sun, NZ Gong, K Ren, C Chen arXiv preprint arXiv:2404.05403, 2024 | 4 | 2024 |