In the first part of the lecture, our researchers talked about Privacy-Preserving Data Publishing (PPDP) and illustrated this topic on the example of mobility data by showing its potential but also highlighting the involved privacy risk when such data is being published. Privacy preserving machine learning  (PPML) on the example of decentralised clinical data was the second focus topic of this lecture. PPML is a subdiscipline of Machine Learning that deals with modelling on sensitive data while preserving their privacy. Privacy and a framework for the evaluation of privacy guarantees were defined and, furthermore, an ensemble learning method for modelling on distributed data sets has been introduced.