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).
Year: 2019
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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.
GRF Research Grant Awarded
I have been awarded an RGC/GRF grant with title “Auditing Machine Learning as a Service” for HK$ 731,089 (2020-2022).
ESORICS Paper Accepted
Our paper with title “A Boundary Differential Private Layer against Machine Learning Model Extraction Attacks” has been accepted by ESORICS 2019. Congratulations to Huadi Zheng!
Best Paper Award
Our paper “Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model” has received the Best Paper Award in 20th IEEE International Conference on Mobile Data Management (MDM’19), Hong Kong.
Call for papers: ACM Transactions on Data Science, Special Issue on Retrieving and Learning from Internet of Things Data
Theme and Topics Internet of Things (IoT) is renovating the way we monitor, understand, and control the physical world. While there are many successful stories on deploying IoT systems in various business sectors, we might underestimate the long-term challenge when facing the explosive amount of IoT and machine-to-machine (M2M) data. Almost all fields of data…
Research Grant Awarded
A research project entitled “AI Model Protection from Inversion Attacks” has been awarded by Huawei with HK$764,520.
IEEE MDM 2019 Call for Advanced Seminars
20th IEEE International Conference on Mobile Data Management (MDM 2019) invites submissions for Advanced Seminar (i.e., Tutorial) proposals on all topics listed in the Call for Papers of the conference. More information can be found in conference website: https://www.comp.hkbu.edu.hk/mdm2019/cfas.php