  Often we consider machine learning models or statistical analysis methods
which we endeavour to alter, by introducing a randomized mechanism, to make the
model conform to a differential privacy constraint. However, certain models can
often be implicitly differentially private or require significantly fewer
alterations. In this work, we discuss Determinantal Point Processes (DPPs)
which are dispersion models that balance recommendations based on both the
popularity and the diversity of the content. We introduce DPPs, derive and
discuss the alternations required for them to satisfy epsilon-Differential
Privacy and provide an analysis of their sensitivity. We conclude by proposing
simple alternatives to DPPs which would make them more efficient with respect
to their privacy-utility trade-off.
