Kok-Seng Wong, PhD
Duyệt theo
Dr. Kok-Seng Wong received his first degree in Computer Science (Software Engineering) from the University of Malaya, Malaysia, and an M.Sc. (Information Technology) degree from the Malaysia University of Science and Technology (in collaboration with MIT). He obtained his PhD degree from Soongsil University, South Korea. He had more than 15 years of teaching record (Computer Science subjects) at universities in Malaysia, South Korea, and Kazakhstan. His research interests lie in applied cryptography, secret sharing, information security, and data privacy.
Các tài liệu mới cập nhật
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Adoption of IP truncation in a privacy-based decision tree pruning design: A case study in network intrusion detection system
(2022-03-04)A decision tree is a transparent model where the rules are visible and can represent the logic of classification. However, this structure might allow attackers to infer confidential information if the rules carry some ... -
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training
(2024-01)In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to use large-capacity neural networks in pursuit of high performance. On the other hand, deep convolutional neural networks (CNNs) ... -
Toward efcient and intelligent video analytics with visual privacy protection for large‐scale surveillance
(2021-05-15)Nowadays, the explosion of CCTV cameras has resulted in an increasing demand for distributed solutions to efficiently process the vast volume of video data. However, the use of surveillance, where people are being watched ... -
Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks
(2022-10-19)Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing ... -
Efficient two-party integer comparison with block vectorization mechanism
(2021-09-13)Private integer comparison has been an essential computation function for many applications, including online auctions, credential identification, data mining, and joint bidding. In the setting of two-party computation, ... -
On the trade-off between privacy protection and data utility for chest X-ray images
(2022)Abstract The advancement of deep learning (DL) techniques has significantly enhanced the accuracy of machine learning (ML) models in medical tasks. However, this progress raises privacy concerns, particularly regarding ... -
A Review of Forest Fire Combating Efforts, Challenges and Future Directions in Peninsular Malaysia, Sabah, and Sarawak
(2022-09-01)The land surface of Malaysia mostly constitutes forest cover. For decades, forest fires have been one of the nation’s most concerning environmental issues. With the advent of machine learning, many studies have been conducted ... -
A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction
(2022-01-17)As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in ... -
Trend Analysis of Forest Fire in Pahang, Malaysia from 2001-2021 with Google Earth Engine Platform
(2022-12)Remote sensing imagery is one of the cost-efficient solutions to observe forest fire occurrence in a particular region. With the accessibility of more public remote sensing data, researchers and field experts can exploit ... -
Toward forecasting future day air pollutant index in Malaysia
(2020-10-14)The association of air pollution and the magnitude of adverse health effects are receiving close attention from the world. The effects of air pollution were found to be most significant for children, elderly, and patients ... -
Improving multi-label text classification using weighted information gain and co-trained multinomial naïve bayes classifier
(2022)Over recent years, the emergence of electronic text processing systems has generated a vast amount of structured and unstructured data, thus creating a challenging situation for users to rummage through irrelevant information. ... -
Few-Shot Learning based on Residual Neural Networks for X-ray Image Classification
(2022)Currently, deep learning is widely used in the field of medicine, which includes radiology. This paper addresses the classification of X-ray images, particularly focusing on the challenge of insufficient images for specific ... -
Emerging Privacy and Trust Issues for Autonomous Vehicle Systems
(2022)In the awakening of cutting-edge technology, companies such as Apple, Waymo, and Tesla are racing to launch the industry’s first fully autonomous car. Besides the technical challenges such as safety and infrastructure, ... -
A Privacy-Preserving Framework for Surveillance Systems
(2020-11)The ability to visually track people present in the scene is essential for any surveillance system. However, the widespread deployment and increased advancement of video surveillance systems have raised awareness of privacy ... -
Benchmarking full version of GureKDDCup, UNSW-NB15, and CIDDS-001 NIDS datasets using rolling-origin resampling
(2021-10)Network intrusion detection system (NIDS) is a system that analyses network traffic to flag malicious traffic or suspicious activities. Several recent NIDS datasets have been published, however, the lack of baseline ... -
Adoption of IP Truncation in a Privacy-Based Decision Tree Pruning Design: A Case Study in Network Intrusion Detection System
(2022-03)A decision tree is a transparent model where the rules are visible and can represent the logic of classification. However, this structure might allow attackers to infer confidential information if the rules carry some ...
