PhD Studentship: A New Lightweight Cryptographic Protocol With Public Blockchain Integration for Privacy Preserving Federated Learning in Resource-Constrained IoT Networks
Found in: beBee jobs MY - 1 week ago
Supervisory Team: Dr. Khor Jing Huey, Dr. Low Siow Yong, Dr. Michail Sidorov (UoSM)
Project description
Federated learning faces security challenges, such as maintaining data privacy and model update integrity. Privacy-preserving aggregation methods, including differential privacy, homomorphic cryptography, and secure multiparty computation have been used to enhance federated learning privacy. However, they require heavy computation, making them unsuitable for resource-limited Internet of Things (IoT) devices. While integrating public blockchain has improved its security, resource-constrained IoT devices may not support this integration, nor complex cryptographic techniques. Therefore, this project proposes a lightweight protocol that incorporates public blockchain to preserve privacy for federated learning in resource-limited IoT networks. It utilizes symmetric key encryption and hash functions to protect local models of resource-constrained IoT devices in federated learning from security attacks. The protocol is modeled using High-Level Protocol Specification Language (HLPSL). The security of the protocol will be analyzed theoretically and formally. A proof-of-concept will be used to verify the security of the proposed protocol with a TensorFlow Federated model. A smart contract will be deployed to a public blockchain for authentication and data integrity purposes, further enhancing the security of the federated learning model. The performance of the protocol in terms of computation and communication costs will be evaluated and optimized. The outcome of the project will be a more secure and efficient protocol with public blockchain integration for privacy-preserving federated learning in resource-constrained IoT networks.
The PhD project will take place entirely at Malaysia campus, where full tuition fee waived. An eligible candidate will also receive a monthly stipend of RM2500. We highly encourage candidates who possess outstanding academic achievements and have a strong background in programming languages to apply for this position. If you are interested, please send your CV and academic transcript to
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: 31 August 2024. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.
Funding: Funding for tuition fees and a living stipend are available on a competitive basis. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
How To Apply
Apply online via the 'Apply' button above. Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select "PhD Engineering & Environment (USoM) (Full time)". In Section 2 of the application form you should insert the name of the supervisor Khor Jing Huey
Applications should include:
Research Proposal
Curriculum Vitae
Two reference letters
Degree Transcripts/Certificates to date
For further information please contact: feps-pgr-
RM2500 monthly stipend
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Malaysia University of Southampton Full timeSupervisory Team: Dr. Khor Jing Huey, Dr. Low Siow Yong, Dr. Michail Sidorov (UoSM) Project description Federated learning faces security challenges, such as maintaining data privacy and model update integrity. Privacy-preserving aggregation methods, including differential privacy, homomorphic cryptography, and secure multiparty computation have been used...
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