  Machine learning models have recently enjoyed a significant increase in size
and popularity. However, this growth has created concerns about dataset
privacy. To counteract data leakage, various privacy frameworks guarantee that
the output of machine learning models does not compromise their training data.
However, this privatization comes at a cost by adding random noise to the
training process, which reduces model performance. By making models more
resistant to small changes in input and thus more stable, the necessary amount
of noise can be decreased while still protecting privacy. This paper
investigates various techniques to enhance stability, thereby minimizing the
negative effects of privatization in machine learning.
