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Biography
Md Newaz Alam is a Postgraduate Research Student within the Department of Electrical Engineering at the University of Southampton Malaysia. He graduated with a BSc in Computer Science, majoring in Software Engineering, from the University Technology Malaysia in 2023. In 2023, he began his PhD research on the topic entitled “A New Lightweight Cryptographic Protocol with Public Blockchain Integration for Privacy-Preserving Federated Learning in Resource-Constrained IoT Networks.”
During his undergraduate studies, Newaz developed a keen interest in software engineering, machine learning, and deep learning. His passion for understanding and applying concepts in computer science, electrical electronics, and microcontroller technologies motivated him to explore advanced research areas. These experiences led him to pursue a PhD under the supervision of Dr. Jing Huey Khor at the University of Southampton Malaysia.
Research Interest
•Federated Learning
•Privacy-Preserving Cryptographic Protocols
•Blockchain Integration
•Resource-Constrained IoT Networks
•Machine Learning
•Deep Learning
Contact
Md Newaz Alam
University of Southampton Malaysia
C0301, C0302, C0401, Blok C Eko Galleria, 3, Jalan Eko Botani 3/2, Taman Eko Botani, 79100 Nusajaya, Johor, Malaysia
Email: M.N.Alam@soton.ac.uk
Current Research Project
Title: A New Lightweight Cryptographic Protocol With Public Blockchain Integration for Privacy Preserving Federated Learning in Resource-Constrained IoT Networks
The research project aims to develop a lightweight protocol that integrates public blockchain to preserve privacy for federated learning in resource-limited IoT networks. Symmetric key encryption and homomorphic hash functions are utilized to secure local models of resource-constrained IoT devices against security attacks. The protocol is modeled using the High-Level Protocol Specification Language (HLPSL) and is analyzed theoretically and formally to ensure its security. A proof-of-concept is employed to verify the protocol's security using a TensorFlow Federated model, and a smart contract is deployed to a public blockchain to enhance authentication and data integrity. The computation and communication costs of the protocol are evaluated and optimized, aiming to provide a secure and efficient solution for privacy-preserving federated learning in IoT networks.
Supervisor: Dr. Khor Jing Huey,Dr.Michail Sidorov,Dr. Low Siow Yong