Isolated ML Training Architecture for Sensitive Data Access

The patent introduces an intermediary training system that sits between the sensitive data storage and the researcher's access. The training system receives encrypted data segments, performs distributed model training using techniques like federated learning and split learning, and returns results without exposing raw data to researchers. This intermediary architecture enables effective model training while maintaining data privacy through cryptographic protections and controlled data flow.

The patent segments the sensitive data into multiple encrypted segments and distributes them across multiple independent storage devices or locations. Each segment alone is insufficient for model training, requiring coordination and communication between multiple devices. This segmentation approach maintains security by ensuring that no single device holds the complete sensitive dataset, while still enabling effective distributed model training through the training system that aggregates results from multiple segments.

The patent implements self-service mechanisms where the distributed training system automatically manages security protocols, encryption, and coordination without requiring manual intervention for each operation. The system includes automated authentication, encrypted communication channels, and built-in security protocols that operate transparently. This reduces operational complexity while maintaining strict security control, as the system enforces security rules automatically rather than requiring human monitoring of each activity.