Widespread sharing of data would facilitate rapid progress in data science. However, due to privacy constraints, sensitive data cannot be made public. My research aims to learn a generative model from the sensitive data under strict privacy guarantees from differential privacy. The generative model is then used to draw a synthetic twin of the data. My focus is on probabilistic generative models that allow quantifying the uncertainty in the model as well as easy interpretability of the process.