  Data protection regulations like the GDPR or the California Consumer Privacy
Act give users more control over the data that is collected about them.
Deleting the collected data is often insufficient to guarantee data privacy
since it is often used to train machine learning models, which can expose
information about the training data. Thus, a guarantee that a trained model
does not expose information about its training data is additionally needed. In
this paper, we present UnlearnSPN -- an algorithm that removes the influence of
single data points from a trained sum-product network and thereby allows
fulfilling data privacy requirements on demand.
