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Master Thesis: Privacy Preserving Machine Learning as a Cloud Service by Fully Homomorphic Encryption

Homomorphic encryption permits computation with encrypted data without having to decrypt it first. It enables the execution of machine learning models on public cloud infrastructures using highly sensitive data such as medical records or business operational data. No sensitive information is leaked and cloud providers or any other third parties are unable to decrypt the data or any results of the machine learning algorithm. The methods are however very computationally demanding and require careful consideration of implementation trade-offs. It is only in recent years that efficient schemes have been developed that are feasible in application.

The objective of the project is to investigate the application of machine learning methods to homomorphically encrypted data and evaluate the consequences for model validation, compute requirements and prediction accuracy.


Scope: 30hp, 1 semester full time

Start date January 2022 (flexible)

Location: Luleå

Compensation: A scholarship of 30,000 SEK (1000 SEK per hp) is granted upon approval of the final report.

This is a challenging thesis project which requires a solid understanding of Mathematics at and beyond the final year level of a Master of Science/Engineering program, as well as some familiarity with common Machine Learning methods and working knowledge in Programming (C/C++, Rust, Python). Basic knowledge of Cryptography is also advantageous.

RISE gather skills and personalities required for the transition to a sustainable economy, society and planet. We devise solutions that can make a difference here and now, while working on research areas and technologies that will be vital for tomorrow. Here, perspectives, expertise and people come together, from technology nerds to ICT experts and computer scientists to microbiologists. We are RISE and we have an offer that is hard to refuse. Challenging assignments that do good.


For questions and further information regarding this project opportunity, reach out to recruiting manager Rickard Brännvall, +46 10 228 44 46.

Applications will be submitted via We like students where you can also find other interesting opportunities.

About the position



Contract type

Temporary position 3-6 months

Job type

Student - Master Thesis/Internship

Contact person

Rickard Brännvall
+46 10 228 44 46

Reference number


Last application date


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