Project Description

The currently announced stipend is connected to the Privacy-aware transparent decisions group at Umeå University. This group (led by Prof. 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 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:

In the project 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 (e.g., transparency and membership attacks). We will develop solutions for centralized and decentralized machine learning (i.e., federated learning). 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.

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