PhD Position in Data Security in Distributed Machine Learning


Website Uppsala Universitet

Project description
Machine learning is becoming a key component in many modern devices and services, ranging from personal mobile devices to autonomous vehicles or smart grids. To improve the underlying machine learning models, it is often crucial that devices share data among each other or with a central authority. This raises several privacy and cybersecurity issues and it must be ensured that both user data remains safe and that user’s privacy is ensured. In this project, we investigate how these challenges can be addressed using modern privacy-preserving approaches such as federated learning, differential privacy and machine learning using encrypted data.

The main duties of this position are to conduct basic as well as applied research in the area of distributed machine learning. Particular focus will be on the development and analysis of distributed inference methods for dynamic systems that ensure data security, for example using federated learning, differential privacy, and homomorphic encryption. The duties also include algorithm implementation in Python and C/C++ as well as collaboration with industry partners.

The duties also include teaching and other departmental duties (no more than 20 %).

You will design the work together with the research team You will be supervised by at least two supervisors. The Department of Electrical Engineering also gives a salary supplement in addition to the local guidelines for doctoral students at Uppsala University.

Find out more and apply at: PhD Position in Data Security in Distributed Machine Learning

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