However distillation is not without privacy risks.
When using data augmentation to create large datasets for distillation, the generated data may inadvertently expose or replicate private information from the original limited private data, raising concerns about the privacy guarantees of the resulting student model.
In the context of machine learning, distillation poses significant privacy risks, as the smaller "student" models or synthesized datasets can inadvertently leak sensitive information about the original, private training data of the "teacher" model. These risks stem from potential vulnerabilities.