Publications

Junxu Liu*, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng.
Cross-silo Federated Learning with Record-level Personalized Differential Privacy.
ACM Conference on Computer and Communications Security (CCS). 2024. (🏆 Distinguished Paper Award)
Junxu Liu*, Mingsheng Xue*, Jian Lou#, Xiaoyu Zhang, Li Xiong, and Zhan Qin.
MUter: Machine Unlearning on Adversarially Trained Models.
International Conference on Computer Vision (ICCV), 2023, pp. 4892-4902.
Junxu Liu*, Jian Lou, Li Xiong, and Xiaofeng Meng#.
Personalized Differentially Private Federated Learning without Exposing Privacy Budgets.

International Conference on Information and Knowledge Management (CIKM), 2023, pp. 4140-4144.

Junxu Liu*, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng#.
Projected Federated Averaging with Heterogeneous Differential Privacy.
International Conference on Very Large Data Bases (VLDB), 2022, 15(4), pp.828-840.
Junxu Liu* and Xiaofeng Meng#.
Survey on Privacy-preserving Machine Learning (in Chinese).
Journal of Computer Research and Development, 2020, 57(2), pp. 346-362. (Top 10 Most Cited Papers in 2020)

Li Bai, Junxu Liu, Sen Zhang, Xinwei Zhang, Qingqing Ye, Haibo Hu
United We Defend: Collaborative Membership Inference Defenses in Federated Learning
USENIX Security Symposium, 2026.
Shiyu Zhang, Qingqing Ye, Junxu Liu, Kai Huang, Yulian Mao, Xinyue Sun, Wei Dong, Haibo Hu.
RATR: Optimized Trajectory Release with Temporal Local Differential Privacy
IEEE Transactions on Dependable and Secure Computing (TDSC), accepted to appear, 2025.
Jinfei Liu*, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, and Jimeng Sun
Dealer: an End-to-End Model Marketplace with Differential Privacy.
International Conference on Very Large Data Bases (VLDB), 2021, 14(6), pp. 957-969.