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Our Research Performance
Members of the GQAS Institute perform various tasks for research
Blockchain-based Delegated Quantum Computing for Secure Industrial IoT Networks Communication
Blockchain-based Delegated Quantum Computing for Secure Industrial IoT Networks Communication
Research Director: Prof. Jong Hyuk Park
Host: National Research Foundation of Korea (NRF), Korea
Period: 2022.12.26 ~ 2025.12.25
Abstract: This study studies the underlying technology for developing a blockchain-based delegated quantum cloud that provides secure authentication between various devices and efficient and secure services of data used in the cloud in a large-scale smart city environment
Real-time face de-identification technology that enables same-subject connection analysis in facial recognition CCTV
Real-time face de-identification technology that enables same-subject connection analysis in facial recognition CCTV
Research Director: Prof. Jong Hyuk Park
Host: Korea Internet & Security Agency (KISA)
Period: 2023.04.01 ~ 2025.12.31
Abstract: This study conducts research on real-time face de-identification technology that enables connection analysis of the same subject to protect personal information in a facial recognition CCTV environment.
Development of a Quantum Machine Learning Solution for Secure and Efficient Cyber Attacks Response
Development of a Quantum Machine Learning Solution for Secure and Efficient Cyber Attacks Response
Research Director: Prof. Jong Hyuk Park
Host: National Research Foundation of Korea (NRF), Korea
Period: 2023.07.01 ~ 2026.06.30
Abstract: This study aims to develop safe and efficient wired and wireless communication networks using next-generation technologies such as quantum machine learning, post-quantum encryption techniques, blockchain, and AI, and secure the source technology of national strategic technology by developing quantum machine learning-based cybersecurity attack technology for sustainable wired and wireless communication network services.
Development of HighReliability Cryptographic Module and Forensic Technology on Energy Cloud
Development of HighReliability Cryptographic Module and Forensic Technology on Energy Cloud
Research Director: Prof. Changhoon Lee
Host: Institute for Information & communication Technology Planning & evaluation (IITP), Korea
Period: 2019.06.20 ~ 2023.12.31
Abstract: It analyzes the structure and characteristics of the energy cloud infrastructure environment and develops a high-trust encryption module considering lightweight encryption module and KCMVP authentication. In addition, it builds cyber threat intelligence that may occur in an energy ICS environment, and develops forensic artifacts collection and analysis methods and forensic analysis and visualization technologies for efficient and systematic accident analysis in the event of an infringement accident.
Development of Fast Design and Implementation of Cryptographic Algorithms based on GPU/ASIC
Development of Fast Design and Implementation of Cryptographic Algorithms based on GPU/ASIC
Research Director: Prof. Changhoon Lee
Host: Institute for Information & communication Technology Planning & evaluation (IITP), Korea
Period: 2021.04.01 ~ 2023.12.31
Abstract: It is a study that derives cryptographic decryption mechanisms for 30 types of encrypted PC files and mobile apps and develops high-speed cryptographic integration systems through the development/implementation of high-speed implementation technologies based on CPU/GPU and FPGA.
Development of Lightweight BIoT technology for Highly Constrained Devices
Development of Lightweight BIoT technology for Highly Constrained Devices
Research Director: Prof. Changhoon Lee
Host: Institute for Information & communication Technology Planning & evaluation (IITP), Korea
Period: 2022.04.01 ~ 2024.12.31
Abstract: Based on lightweight security/authentication protocol technology, we develop security protocols for BIoT (off-chain microtransaction technology, BIoT lightweight security/authentication/access control protocol technology).
Research on AI-based Encrypted Traffic Analysis Techniques for Responding to Encrypted Threats
Research on AI-based Encrypted Traffic Analysis Techniques for Responding to Encrypted Threats
Research Director: Prof. Ki Wook Sohn
Host: Institute for Information & communication Technology Planning & evaluation (IITP), Korea
Period: 2023.07.01 ~ 2023.12.31
Abstract: It is a study to develop AI-based threat detection techniques for encrypted traffic by developing feature extraction techniques based on packet structure and traffic characteristics of cryptographic communication protocols and applications and metadata and statistical characteristics of encrypted traffic.
Explainable Lightweight AI Based on Multimodal Data for Precise Life-log Analysis
Explainable Lightweight AI Based on Multimodal Data for Precise Life-log Analysis
Research Director: Prof. Eun Som Jeon
Host: National Research Foundation of Korea (NRF), Korea
Period: 2026.03.01 ~ 2030.02.28
Abstract: This study aims to establish a multimodal XAI-based knowledge distillation framework that integrates and analyzes lifelog data from sensors, images, and text, enabling the development of high-performance lightweight models by simultaneously capturing both the structural and semantic information involved in behavior analysis during the model compression process. Topology-based structural representations and semantic representations are aligned with the intermediate representations through explainable concept-level learning to enhance the model’s interpretability and generalization capability. This study finally aims to develop a robust lightweight AI model.
On-device AI Analysis Solution for Large Vehicle Accident Prevention
On-device AI Analysis Solution for Large Vehicle Accident Prevention
Research Director: Prof. Eun Som Jeon
Host: Technology and Information Promotion Agency for SMEs, Korea
Period: 2025.07.01 ~ 2026.12.31
Abstract: This study aims to develop commercial products for large vehicle accident prevention in diverse environments, as well as lightweight models for real-time processing. Lightweight models are required to enable real-time accident prevention, and datasets containing various environmental conditions are utilized to recognize diverse situational factors. In addition, this study seeks to develop a generalized modeling methodology that can be broadly applied across different environments and vehicle types.
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