What if you could model with sensitive data that you can't see? This session introduces analytical embedding spaces—a technique that transforms raw data into compressed synthetic trends, allowing insights to be gained from information while maintaining segregation between modelers and sensitive data . By modeling in this transformed space, we capture the signal without compromising the source!

We'll show a use case where healthcare data is off-limits, but consumer data is available. By embedding both datasets and modeling with synthetic trends, we demonstrate that "sight unseen" modeling can outperform traditional methods - improving disease prediction without ever accessing the raw health data. This approach stems from federated learning, where insights are derived without  accessing sensitive information.

Designed for secure AI, this approach respects data and protects against reconstruction attacks. Embeddings act as a privacy-preserving interface, ensuring that even if adversaries gain access to the model, they cannot reverse-engineer the original inputs. This framework is particularly powerful in regulated industries, where data utility and compliance need to coexist.If you've ever been told, "You can't use that data," this session will show you how you can  - just not the way you thought you could.