Artificial intelligence systems are increasingly trained using datasets containing private information about individuals in critical areas such as government services, healthcare, and education. However, these AI systems have a demonstrated risk of accidentally revealing sensitive personal information about the people whose data was used during training, creating serious privacy and security concerns. This problem threatens public trust in AI technologies and creates barriers to beneficial uses of AI in sensitive domains where privacy protection is essential. Currently, many organizations cannot safely use AI because existing privacy protection methods are either inadequate or too difficult to implement correctly. This project addresses this challenge by developing freely available software tools that prevent these privacy vulnerabilities in future AI systems. These tools will make state-of-the-art privacy protection methods practical and accessible to a broad community of developers and researchers. This work serves the national interest by advancing privacy protection for all citizens, strengthening trust in AI technologies used by government and industry, supporting American competitiveness in privacy-preserving AI development, and enabling secure use of AI in critical national infrastructure while protecting individual rights. This project advances privacy-preserving machine learning by developing and implementing novel techniques for training large models with the str