How Vivax Protects Hospital Data: Our Privacy-First AI Stack
— by Vivax
Trustworthy medical AI isn't one technique — it's a layered defense. At Vivax we combine de-identification, differential privacy, secure aggregation,…
There is no single switch that makes medical AI 'private.' Real protection comes from defense in depth — several independent safeguards layered so that no one failure exposes patient data. Over recent weeks we have written about each layer on its own: de-identification, differential privacy, secure aggregation, federated learning, and confidential computing. This is how Vivax brings them together.
It starts with the data itself. Our Standardized Data Platform grades every record by quality and reliability, then cleans, validates, and harmonizes it into structured, unified forms — with open-source data archetypes others can build on. De-identification is the baseline, and a new device-and-software standard minimizes the middle layers between hardware, records, and the model, reducing the number of places data is copied or exposed.
On top of that foundation we are now setting up federated learning, differential privacy, and secure aggregation for model enhancements at the hospitals already using the Vivax clinic world model. In plain terms: each hospital trains locally, only privacy-bounded and cryptographically masked updates are shared, and the coordinating server learns a better shared model without ever seeing one patient's record. Confidential computing protects the computation itself, so data stays encrypted even while it is being used.
The payoff is a model that gets smarter from many hospitals' experience while every institution keeps full custody of its data. That is the only foundation on which decision-grade clinical AI can responsibly be built — and it is the standard we hold ourselves to as we ground the Vivax clinical world model in the real world of medicine.