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).
I received ICDE 2020 Outstanding Reviewer Award among six other reviewers out of 200+ TPC members.
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.
I have openings for 1~2 PhD students (2021 in-take), 2~3 research assistants, and 4 postdoc researchers in the field of machine learning, data security and privacy. The detailed requirements are as follows:
- Bachelor or Master degree in Computer Science, Software Engineering or Information Engineering in well-known universities. Preferences are given to applicants with some research experience.
- Good programming skills in at least one mainstream language such as Java, Python and C++.
- Good reading/writing skills in English. Preferences are given to applicants who score 80+ in TOEFL (iBT) or 6.5+ in IELTS.
Remuneration is highly competitive.
- For PhD students, if you are awarded Hong Kong PhD Fellowships (HKPFS), you can receive 25.8k HKD (equiv. to 3.2k USD) monthly and other matching benefits by the university/department.
- For research assistants, the monthly salary is 18k HKD for bachelor degree holders) and 21k HKD for master degree holders.
- For Postdocs, the monthly salary is fixed at 32k HKD (equiv. to 4k USD).
Those interested please send your CV, publication, and/or transcripts to Dr. Haibo Hu at firstname.lastname@example.org
1. Dr. Haibo Hu’s website： www.haibohu.org
2. Applied Security, Trust and Privacy Lab: http://www.astaple.com/
3. Information about HKPFS application with Hong Kong Polytechnic University: http://www.polyu.edu.hk/ro/hkphd-fellowship/
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).
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.
I have been awarded an RGC/GRF grant with title “Auditing Machine Learning as a Service” for HK$ 731,089 (2020-2022).
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!
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.
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 science need new IoT-aware solutions, including but not limited to data acquisition, cleaning, transformation, storage, integration, indexing, modeling, analysis, visualization, and interpretation.
The main focus of this special issue will be on the identification of problems in adopting existing data science techniques for IoT/M2M data and the renovation on them, with an emphasis on the retrieval and learning from these data. We welcome papers on new problems, techniques, methodologies and research directions for open problems in the context of IoT. Topics of interest include, but are not limited to:
- Data acquisition and cleaning techniques for IoT
- Data storage and indexing techniques for IoT
- Data modeling, representation, transformation, and integration techniques for IoT
- Streaming and query processing techniques for IoT
- Data mining and machine learning algorithms for IoT Big Data
- Data visualization and interpretation techniques for IoT
- Security, privacy and trust in Internet of Things
- Uncertainty and probabilistic IoT Big Data
- Data-driven IoT system design, implementation, and deployment
- Open issues for data management and analytics in IoT
- Manuscript Due: Aug 1, 2019
- First Notification: Nov 1, 2019
- Revised Manuscript: Feb 1, 2020
- Notification of Acceptance: Apr 1, 2020
- Camera Ready Paper Due: Jun 1, 2020
All submissions have to be prepared using the ACM template and submitted as a PDF via the Manuscript Central submission site at https://mc.manuscriptcentral.com/tds. More details about the submission can be found at https://tds.acm.org/authors.cfm.
- Haibo Hu, The Hong Kong Polytechnic University, China (email@example.com) web: http://haibohu.org
- Rik Sarkar, The University of Edinburgh, UK (firstname.lastname@example.org) web: https://homepages.inf.ed.ac.uk/rsarkar/
- Zhengzhang Chen, NEC Laboratories America, US (email@example.com) web: http://www.nec-labs.com/zhengzhang-chen