  Machine Learning on Big Data gets more and more attention in various fields.
Even so privacy-preserving techniques become more important, even necessary due
to legal regulations such as the General Data Protection Regulation (GDPR). On
the other hand data is often distributed among various parties. Especially in
the medical context there are several data holders, e.g. hospitals and we need
to deal with highly sensitive values. A real world scenario would be data that
is held in an electronic patient record that is available in many countries by
now. The medical data is encrypted. Users (e.g. physicians, hospitals) can only
decrypt the data after patient authorization. One of the main questions
concerning this scenario is whether it is possible to process the data for
research purposes without violating the privacy of the data owner. We want to
evaluate which cryptographic mechanism - homomorphic encryption, multiparty
computation or trusted execution environements - can be used for this task.
