It’s hard to understate the impact machine learning will have on biomedicine. The ability to train computers to spot patterns by analyzing large, complex datasets is driving discoveries in heart disease, cancer, neurodegenerative diseases and more. For instance, Argonne National Laboratory (ANL) has used machine learning to aid cancer research and accelerate COVID-19 antiviral discovery. One of the main challenges is finding ways to share patient information between research organizations without violating privacy regulations. The Health Insurance Portability and Accountability Act has strict rules on how organizations can share patient data. The PALISADE-X project is working to solve this issue in order to improve research outcomes. PALISADE-X stands for Privacy-preserving Analysis and Learning in Secure and Distributed Enclaves and Exascale Systems. The project is funded by the DOE Office of Science’s Office of Advanced Scientific Computing Research with a goal of creating and experimenting with secure computing technologies and privacy enclaves—a way to isolate code and data from an operating system—to store and analyze PII (personally identifiable information) data. Led by ANL, the PALISADE-X team includes collaborators from LLNL, the University of Chicago, and the Broad Institute. Read more from ANL news.