Tran Ngoc Phuong
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: National Institute Of Hygiene And Epidemiology (2021–2023); Agricultural Biotechnology Institute (2019); HUN-REN Veterinary Medical Research Institute (2015)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Tran Ngoc Phuong's research has focused on developing advanced cryptographic techniques for secure data sharing and privacy preservation, particularly within cloud computing and the Internet of Things (IoT). His work includes creating novel schemes for attribute-based encryption, proxy re-encryption, and message authentication that aim to enhance data security under standard assumptions. Phuong has investigated methods for privacy-preserving machine learning, such as the GELU-Net system, which allows for encrypted deep neural networks. He has also explored efficient methods for secure data delivery and similarity computations in IoT environments. His research network includes collaborators from the University of Arkansas at Little Rock, with whom he has co-authored multiple publications. Phuong's scholarly output is reflected in an h-index of 7 and over 100 citations across his published works.
Metrics
- h-index: 7
- Publications: 17
- Citations: 119
Positions
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Assistant Professor 2024–presentUniversity of Arkansas at Little Rock Department of Computer Science ORCID
Selected Publications
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PIPE4EQ: Practical Secure ECG Abnormality Queries by Cloud-Assisted Biosignal Monitoring (2026)
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One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoT (2025)
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Practical Inner Product Encryption for Privacy-Preserved Internet-of-Things Applications (2025)
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CFE: Secure Filtered Words in End-to-End Encrypted Messaging System (2024)
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A Privacy-Preserving Cyber Threat Intelligence Sharing System (2024)
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Short paper: Secure Lightweight Computation for Federated N-Gram Language Model (2024)
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Breaking Privacy in Model-Heterogeneous Federated Learning (2024)
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Secure Lightweight Data Communication Between the IoT Devices and Cloud Service (2024)
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Anonymous attribute-based broadcast encryption with hidden multiple access structures (2024)
Collaboration Network
Top Collaborators
- Secure Lightweight Data Communication Between the IoT Devices and Cloud Service
- Short paper: Secure Lightweight Computation for Federated N-Gram Language Model
- CFE: Secure Filtered Words in End-to-End Encrypted Messaging System
- A Privacy-Preserving Cyber Threat Intelligence Sharing System
- CFE: Secure Filtered Words in End-to-End Encrypted Messaging System
- Secure Lightweight Data Communication Between the IoT Devices and Cloud Service
- Breaking Privacy in Model-Heterogeneous Federated Learning
- Breaking Privacy in Model-Heterogeneous Federated Learning
- Breaking Privacy in Model-Heterogeneous Federated Learning
- Breaking Privacy in Model-Heterogeneous Federated Learning
- A Privacy-Preserving Cyber Threat Intelligence Sharing System
- A Privacy-Preserving Cyber Threat Intelligence Sharing System
- CFE: Secure Filtered Words in End-to-End Encrypted Messaging System
- CFE: Secure Filtered Words in End-to-End Encrypted Messaging System
- Practical Inner Product Encryption for Privacy-Preserved Internet-of-Things Applications
- Practical Inner Product Encryption for Privacy-Preserved Internet-of-Things Applications
- One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoT
- PIPE4EQ: Practical Secure ECG Abnormality Queries by Cloud-Assisted Biosignal Monitoring
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