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Privacy-preserving systems around trust and identity within smart phones

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  • Full or part time
    Prof B Buchanan
    Dr NIKOLAOS Pitropakis
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Blockpass Identity Lab at Edinburgh Napier University, The Data Labs and Sitekit Systems Ltd, t/a Condatis are collaborating on an industry-focused PhD programme and which focuses on privacy-preserving behavioural analysis and biometric methods. Condatis currently employs around 50 highly-skilled staff, predominantly in Edinburgh and the Isle of Skye. Condatis is part of the Sitekit Ltd Group, which was established 1989 and is now a recognised digital identity UK-market leader. The focus for the PhD is around how we can identify users using their behaviour patterns and in biometric methods, but how we can respect the privacy of the user. It aims to apply novel methods in cryptography and in machine learning, in order to protect the sensitive data gathered on a smart phone, but still allow for machine learning.

Academic qualifications
A first degree (at least a 2.1) or MSc ideally in Computer Science-related area 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.

Essential attributes:
• Strong AI/Machine Learning skills
• Competent in programming
• Strong focus on applying computer security concepts.
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good organisation and time management skills

Desirable attributes:
• Excellent in programming and software testing
• Experience developing back-end web-based applications, using web APIs
• Knowledge of cryptography fundamentals and their application
• Experience with biometric authentication using AI/ML

Funding Notes

This is a funded position.

References

Murmuria, R., Stavrou, A., Barbara, D., & Sritapan, V. (2017, March). Your data in your hands: Privacy-preserving user behavior models for context computation. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 170-175). IEEE.

Gai, K., Qiu, M., & Zhao, H. (2017). Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Transactions on Big Data.

Mohassel, P., & Zhang, Y. (2017, May). Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 19-38). IEEE.



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