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 | 232 | 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 | 73 | 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 | 58 | 2020 |
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 | 51 | 2019 |
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 | 44 | 2018 |
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 | 42 | 2018 |
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 | 37 | 2021 |
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 | 37 | 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 | 30 | 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 | 28 | 2023 |
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 | 26 | 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 | 24 | 2020 |
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, 2023 | 9 | 2023 |
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, 2023 | 4 | 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 | 4 | 2023 |
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 | 2 | 2023 |
SoK: Gradient Leakage in Federated Learning J Du, J Hu, Z Wang, P Sun, NZ Gong, K Ren arXiv preprint arXiv:2404.05403, 2024 | | 2024 |
Towards Efficient Edge Learning for Large Models in Heterogeneous Resource-limited Environments D Liu, Z Wang, X Pang, Y Sun, J Hu, P Sun, Y Hu 2023 9th International Conference on Big Data Computing and Communications …, 2023 | | 2023 |
User Privacy Protection in MCS: Threats, Solutions, and Open Issues Z Wang, X Pang, P Sun, J Hu Mobile Crowdsourcing: From Theory to Practice, 321-355, 2023 | | 2023 |