PhD student position in Computing Science with focus on data privacy at the Department of Computing Science, Umeå University.
This is a project in collaboration with Chalmers and KTH. We will develop machine learning algorithms that build data-driven models avoiding disclosure of private information and that are resistant to different types of attacks (t ex. transparency and membership attacks). The objective is to build statistical and machine learning models taking into account different types of privacy models (differential and integral privacy, k-anonymity), as well as different types of scenarios (centralized and decentralized data). The project will consider federated learning approaches. Models are expected to follow trustworthy AI principles, and, in particular, take into account explainability. These models are attractive because they allow people to understand why decisions are made, but at the same time explainability implies additional privacy threats to be tackled. Data-driven models are to be applied to IoT (Internet of Things) scenarios.
The Privacy-aware transparency decisions research group (led by Professor Vicenç Torra) conducts research in data privacy for data to be used for machine and statistical learning. It is well known that data can be highly sensitive, and that naive anonymization is not sufficient to avoid disclosure. Models and aggregates can also lead to disclosure as they can contain traces of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major journals on data privacy (Transactions on Data Privacy), and has active links with the private and public sectors. For more information see https://www. umu.se/en/research/groups/nausica-privacy-aware-transparent-decisions-group-/