The growing use of artificial intelligence in healthcare and medical research has created a difficult challenge: researchers need to share their computer models to advance scientific discovery, but these models can reveal private information about the patients whose data was used to create them. Organizations are often reluctant to share their computer models because of privacy risks, even though withholding these models prevents broader societal benefits from medical research. This creates a barrier to scientific collaboration that could otherwise lead to better treatments, improved public health outcomes, and medical breakthroughs. This project addresses this challenge by developing methods that allow organizations to safely share models trained on sensitive patient data without compromising individual privacy. Proposed research will result in new techniques for auditing models, certifying their privacy guarantees, and providing actionable tools to fix any identified issues. This work serves the national interest by advancing medical research and scientific discovery, enhancing national health and prosperity through improved healthcare technologies, supporting American competitiveness in artificial intelligence innovation, and enabling secure collaboration while protecting personal privacy rights. This project develops an end-to-end framework for privacy-preserving sharing of machine learning models trained on sensitive data. Despite growing interest in sharing models ra