Category: achievement

Our paper “PrivRM: A Framework for Range Mean Estimation under Local Differential Privacy” is accepted by ACM International Conference of Management of Data (SIGMOD), 2025. Congratulations to Liantong! Our paper “Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services” is accepted by Proceedings of the VLDB Endowment (PVLDB), 2025. Congratulations to Rong!

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). Congratulations to Xinwei and Zitong!

Our papers “A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks” and “Exploring Intrinsic Alignments within Text Corpus” are accepted by Annual AAAI Conference on Artificial Intelligence (AAAI 2025). Congratulations to Haoyang and Zi!

Our papers “Structure-Preference Enabled Graph Embedding Generation under Differential Privacy” and “Data Poisoning Attacks to Local Differential Privacy Protocols for Graphs” are accepted by IEEE International Conference on Data Engineering (ICDE), 2025. Congratulations to Sen and Kai!

Our paper “PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy” is accepted by SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025). Congratulations to Sen!

Our paper “Federated Heavy Hitter Analytics with Local Differential Privacy” is accepted by International Conference of Management of Data (SIGMOD 2025)”. Congratulations to Yuemin! Our paper “Membership Inference Attacks and Defenses in Federated Learning: A Survey” is accepted by ACM Computing Surveys. Congratulations to Li!

Prof. Hu has been awarded an ITF grant with project title “MyGPTShield: A Personalized Privacy-Preserving Prompt Service for Large Generative AI Models”. The total amount is HK$2,000,010.

Prof. Hu has been awarded an RGC/GRF grant with project title “Right To Be Forgotten Made Easy: Machine Unlearning, Differential Privacy and Beyond”. The total amount is HK$1,038,967. Dr. Ye has been awarded an RGC/ECS grant with project title “Harnessing Sensitive Statistics from the Crowd: Towards Scalable Private Federated Analytics”. The total amount is HK$992,994.

Thanks to the outstanding rating of her research grant proposal in the Early Career Scheme, Dr. Ye Qingqing has received the Early Career Award from UGC this year. Every year, there are only 7-8 awardees in all 8 public universities in Hong Kong.