Privacy-preserving Systems around Security, Trust and Identity

About the Project

Blockpass and the School of Computing at Edinburgh Napier University have set up an advanced Blockchain Identity Lab (BIL), which supports world-leading research related to cryptography, blockchain, distributed ledger technologies, privacy-preserving machine learning, and their linkage to sovereign identities such as decentralised identities and verifiable credentials. It currently supports several PhD studentships, and it aims to increase this number.

Successful applicants in this role will investigate, but are not limited to, a wide range of areas related to security, privacy, identity, trust/consent/delegation, AI and secure software development processes. A key focus will be on privacy-preserving methods, trusted smart contracts, anonymised machine learning, and the integration of trust, governance and consent around distributed models.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements ideally in Computer Science with a good fundamental knowledge of computer science and computer security.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Fundamental knowledge:

• Computer Science
• Computer Security
• Blockchain
• Distributed Ledger Technologies
• Privacy-Preserving Machine Learning
• AI Security
• Secure Software Development
• Software Security
• AI for Cybersecurity
• Cybersecurity for AI

Essential attributes:

• Experience of fundamental computer science areas, including a background in computer security
• Competent in programming and software testing
• Knowledge of computer security methods, including the fundamentals of cryptography
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:

• A strong desire to build trusted architectures, which integrate privacy and trust

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use the advertised title as project title

For informal enquiries about this PhD project, please contact P.Papadopoulos@napier.ac.uk

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc

PhD Start Date: October 2026

Funding Notes

International applicants should note that visa application costs and the NHS health surcharge are additional costs to be taken into consideration, and successful applicants will need to cover these expenses themselves.

References

• Bernabe, J. B., Canovas, J. L., Hernandez-Ramos, J. L., Moreno, R. T., & Skarmeta, A. (2019). Privacy-preserving solutions for blockchain: Review and challenges. IEEE Access, 7, 164908-164940.
• Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., ... & Yellick, J. (2018, April). Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proceedings of the thirteenth EuroSys conference (pp. 1-15).
• Yin, X., Zhu, Y., & Hu, J. (2021). A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys (CSUR), 54(6), 1-36.
• Mohammed, N. M., Niazi, M., Alshayeb, M., & Mahmood, S. (2017). Exploring software security approaches in software development lifecycle: A systematic mapping study. Computer Standards & Interfaces, 50, 107-115.
• Corallo, A., Lazoi, M., & Lezzi, M. (2020). Cybersecurity in the context of industry 4.0: A structured classification of critical assets and business impacts. Computers in industry, 114, 103165.
• Kurakin, A., Goodfellow, I. J., & Bengio, S. (2018). Adversarial examples in the physical world. In Artificial intelligence safety and security (pp. 99-112). Chapman and Hall/CRC.
• Bertino, E., Kantarcioglu, M., Akcora, C. G., Samtani, S., Mittal, S., & Gupta, M. (2021, April). AI for Security and Security for AI. In Proceedings of the eleventh ACM conference on data and application security and privacy (pp. 333-334).