One Paper Accepted in IEEE TDSC
Our paper “Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation” has been accepted for publish in IEEE Transactions on Dependable and Secure Computing.
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New NSFC Project Awarded
I have been awarded a new NSFC project (面上项目, 2021-2024) with title “机器学习即服务中的防欺诈和完整性验证研究”, for 570,000 RMB.
Two Papers Accepted in IEEE Intelligent Systems and CIKM 2020
Our paper “Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning? ” has been accepted for publish in IEEE Intelligent Systems. Our paper “OHEA: Secure Data Aggregation in Wireless Sensor Networks against Untrusted Sensors” has been accepted for publish in ACM CIKM 2020.
GRF Research Grant Awarded
A research project entitled “Securing Models and Data for Machine Learning at the Edge” has been awarded by Research Grant Council, HKSAR with HK$845,055 (2021-2023).
ITF Grant Awarded
I have been awarded an ITF grant from Hong Kong government with title ” vMPOS: Virtual Mobile POS using Smartphone and Near Field Communication” for HK$ 3,927,250 (2020-2023).
ICDE 2020 Outstanding Reviewer Award
I received ICDE 2020 Outstanding Reviewer Award among six other reviewers out of 200+ TPC members.
Two Papers Accepted in ICDE 2020 and ICDCS 2020
Our paper “Towards Locally Differentially Private Generic Graph Metric Estimation” has been accepted for publish in IEEE ICDE 2020. Our paper “Cloud Password Shield: A Secure Cloud-based Firewall against DDoS on Authentication Servers” has been accepted for publish in IEEE ICDCS 2020.
One Paper Accepted in TIFS
Our paper “MISSILE: A System of Mobile Inertial Sensor-Based Sensitive Indoor Location Eavesdropping.” has been accepted for publish in IEEE Transactions on Information Forensics and Security (TIFS).
Best Paper Award
Our paper “Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning?” has received the Best Theory Paper Award in the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19), in conjunction with IJCAI’19.