Summary
Modern applications of AI and machine learning in fields such as genomics, neuroscience, healthcare, and social sciences depend on the analysis of vast high-dimensional datasets that often include highly sensitive personal information. As AI systems rely more on data, achieving high predictive accuracy is no longer enough. Machine learning algorithms must also ensure privacy and remain computationally efficient at scale. This project investigates the fundamental trade-offs between accuracy, privacy, and computational efficiency, aiming to establish a mathematically sound foundation for trustwo
