PE can address privacy concerns related to the pre-training data of foundation models by ensuring that the private data used in the PE algorithm has no overlap with the pre-training data of the foundation models. This is crucial for maintaining privacy and preventing any potential breaches or leaks of sensitive information. By using APIs from blackbox models, which do not reveal their training datasets, users can run PE safely as long as they ensure that their private data has never been shared or posted online. For local models where users have full control over model weights and architectures, they can pre-train the models on non-overlapping data to guarantee privacy.