TBSI Student wins best oral presentation award at IEEE International Conference on Big Data Analysis

Master’s student Juting Wang of TBSI Prof. Wai Kin Victor Chan's research group recently won Best Oral Presentation Award at the 5th IEEE International Conference on Big Data Analytics (IEEE ICBDA 2020). Wang is the first author of the paper “TPEGADP: improvement of EGADP based on topology potential”, and completed the award-winning paper under the guidance of Prof. Chan.

The IEEE Conference was held in Xiamen, China on May 8- 11 and is a leading forum for disseminating latest research in Big Data Research, Development, and Application. The conference is co-sponsored by the Research Institute of Big Data Analytics of Xi'an Jiaotong-Liverpool University (XJTLU) and the School of informatics of Xiamen University, and assisted by Hong Kong Polytechnic University (PolyU) and University of Texas at Dallas. XJTLU Prof. Steven Guan and PolyU Prof. Qing Li were Co-chairs of the conference.

Juting Wang’s paper addresses current limitations in data perturbation methods that are used to protect confidential information in datasets. Wang surveys TPEGADP, a data perturbation method that involves topology potential of different areas to calculate the density of the k neighborhood. Data can be perturbated according to the density of specific point sets, and cluster features and statistics are also maintained during encryption.

Prof. Wai Kin Victor Chan’s Intelligent Transportation and Logistics Systems Lab performs theoretical research and application technology on key factors such as efficiency, safety and environmental protection of transportation, logistic, and industrial systems under systems thinking, optimization, intelligent instruments, and big data technology.

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Award certificate


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Juting Wang's presentation


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Juting Wang  (fifth row) during the online presentation session




Source: TBSI, ICBDA Website

Editor: Karen Lee






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