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Critical infrastructure such as healthcare, energy grids, industrial systems, and defence applications are increasingly using smart devices (IoT) and virtual simulations (digital twins) for real-time monitoring and decision-making. However, protecting data privacy in these environments is essential. Since IoT devices are small and lack significant processing power, they cannot handle complex tasks, so these tasks are offloaded to digital twins, which have greater computing capability. To keep data private during this transfer, advanced encryption is used, with “homomorphic encryption” showing considerable promise.
Privacy-preserving machine learning (PPML) enables these systems to work with data that remains encrypted, meaning the original data is never exposed, even while being analysed. For example, in healthcare, patient data can stay encrypted while it is used to train models to predict disease. In energy grids, operational data can also remain encrypted while analysed for potential issues without revealing sensitive information.
Homomorphic encryption is particularly valuable in healthcare, where patient privacy is heavily regulated (such as by GDPR in Europe). For instance, if IoT devices track patient health data, this information can be sent in encrypted form to a digital twin for analysis. This allows the system to alert clinicians to critical health changes such as early signs of heart failure while ensuring patient confidentiality and meeting regulatory standards.
The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
This project is funded by:
Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of £900 per annum is also available.
These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies.
1. Hijazi, N.M., Aloqaily, M., Guizani, M., Ouni, B. and Karray, F., 2023. Secure federated learning with fully homomorphic encryption for iot communications. IEEE Internet of Things Journal.
2. Qu, X., Hu, Q. and Wang, S., 2020. Privacy-preserving model training architecture for intelligent edge computing. Computer Communications, 162, pp.94-101.
3. Zhang, L., Xu, J., Vijayakumar, P., Sharma, P.K. and Ghosh, U., 2022. Homomorphic encryption-based privacy-preserving federated learning in IoT-enabled healthcare system. IEEE Transactions on Network Science and Engineering, 10(5), pp.2864-2880.
4. Xie, Q., Jiang, S., Jiang, L., Huang, Y., Zhao, Z., Khan, S., Dai, W., Liu, Z. and Wu, K., 2024. Efficiency optimization techniques in privacy-preserving federated learning with homomorphic encryption: A brief survey. IEEE Internet of Things Journal, 11(14), pp.24569-24580.
5. Yang, W., Wang, H., Li, Z., Niu, Z., Wu, L., Wei, X., Su, Y. and Susilo, W., 2024. Privacy-Preserving Machine Learning in Cloud-Edge-End Collaborative Environments. IEEE Internet of Things Journal.
6. Lin, Y., Chen, L., Ali, A., Nugent, C., Cleland, I., Li, R., Ding, J. and Ning, H., 2024. Human digital twin: A survey. Journal of Cloud Computing, 13(1), p.131.
7. Aaqib, M., Ali, A., Chen, L. and Nibouche, O., 2024, August. Explainable Ensemble-Based Trust Management for IoT Systems. In The International Conference on Innovations in Computing Research (pp. 732-742). Cham: Springer Nature Switzerland.
8. Aaqib, M., Ali, A., Chen, L. and Nibouche, O., 2024, June. Behavior-Based Interpretable Trust Management for IoT Systems. In 2024 35th Irish Signals and Systems Conference (ISSC) (pp. 1-6). IEEE.
9. Aaqib, M., Ali, A., Chen, L. and Nibouche, O., 2023. IoT trust and reputation: a survey and taxonomy. Journal of Cloud Computing, 12(1), p.42.
10. Aaqib, M., Ali, A., Chen, L. and Nibouche, O., 2023, August. Discriminative features-based trustworthiness prediction in IoT devices using machine learning models. In 2023 IEEE Smart World Congress (SWC) (pp. 1-6). IEEE.
Submission deadline
Monday 24 February 2025
04:00PM
Interview Date
April 2025
Preferred student start date
15 September 2025
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