Rapid proliferation of Machine Learning (ML) systems in today online services and applications have given rise to privacy preserving machine learning research field. In the tutorial we present a primer understanding of privacy preserving ML system design approaches, by drawing in the knowledge from the state-of-the art private learning methods. We present the primer understanding in the tutorial session that is part of the IFIP summer school, which included an interactive feedback discussion session. The tutorial participants range from students to experts in various different research fields and indicated their interest in the topic. The tutorial format consists of i) presentation of the tutorial topic and ii) interactive discussion session to encourage the participants to actively discuss/reinforce their understanding and operational concerns of the tutorial topics.