
GRF Research Grant Awarded
A research project entitled “Privacy-Preserving Multimodal Data Management: A Database Perspective” has been awarded by Research Grant Council, HKSAR with HK$854,554 (2026-2028).
A research project entitled “Privacy-Preserving Multimodal Data Management: A Database Perspective” has been awarded by Research Grant Council, HKSAR with HK$854,554 (2026-2028).
I have openings for 2~3 PhD students (2026 in-take), and 4+ research assistants/postdoc researchers (immediately available) in the field of machine learning, data security and privacy. The detailed requirements of PhD applicants are as follows: Bachelor or Master degree in Computer Science, Software Engineering or Information Engineering in well-known universities. Preferences are given to applicants…
Our papers “‘Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy” and “Federated Continuous Category Discovery and Learning” are accepted by International Conference on Computer Vision (ICCV), 2025. Congratulations to Yaxin and Lixiu!
Our paper “PrivAGM: Secure Construction of Differentially Private Directed Attributed Graph Models on Decentralized Social Graphs” is accepted by VLDB 2025.
Our paper “‘Yes, My LoRD.’ Guiding Language Model Extraction with Locality Reinforced Distillation” is accepted by the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) Main Conference. Congratulations to Zi!
Our paper “Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?” is accepted by 42nd International Conference on Machine Learning (ICML ’25).
In addition to the previous news that two papers were accepted by ICDE 2025 in round 1, five more papers below are accepted in round 2.
Our paper “Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy” is accepted by IEEE Symposium on Security and Privacy (S&P), 2025.
Our paper “Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services” is accepted by The Proceedings of the VLDB Endowment (PVLDB), 2025.
Our papers “MER-Inspector: Assessing Model Extraction Risks from An Attack-Agnostic Perspective” and “FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning” are accepted by ACM TheWebConf 2025 (WWW 2025).