There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service providers and there is a risk of data breaches. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning (ML) system could lead to services that benefit all. For instance, consider a consortium of banks that want to improve fraud detection by pooling their customers’ payment data
and merge these with data from, e.g., Statistics Denmark.
However, for competitive reasons the banks want to keep their customers’ data secret and Statistics Denmark is not allowed to share the required sensitive data. As another example, consider patient information (e.g., medical images) stored at hospitals. It would be great to build diagnostic and prognostic tools using ML based on these data, however, the data can typically not be shared.