Confidential Computing & Secure Learning Environments
Establishing secure computing environments for advanced analytics on highly sensitive biomedical datasets
I led the creation of the Argonne Biomedical Learning Enclave, a secure computing environment designed to support advanced analytics and machine learning over highly sensitive biomedical datasets.
Architecture
This work combined confidential computing, secure enclaves, and policy-controlled access mechanisms to enable learning on protected health and genomic data while meeting strict security and compliance requirements.
Key Features
- Confidential computing with secure enclaves
- Policy-driven governance and access control
- Integration with DOE supercomputing resources
- Secure hardware and software co-design
- HIPAA and PHI compliance
Impact
By integrating these capabilities with DOE supercomputing resources, this effort demonstrated how secure hardware and software co-design can support scalable analytics without exposing raw data. The enclave model has enabled new collaborations that were previously infeasible due to data sensitivity constraints.
This contribution has influenced emerging best practices for secure AI and analytics in regulated biomedical research environments.
Applications
- VA Million Veteran Program data analysis
- Protected health information (PHI) analytics
- Multi-institutional genomic studies
- Sensitive clinical data modeling