AI is powerful, but one problem has always remained:
👉 How do we prove a model was trained correctly without exposing sensitive data?
That’s exactly what @Lagrange Official is solving with Proofs of Training — one of the four ZK proof types they’re building.
Here’s what it enables:
Verifiable training across parties – everyone can trust the result without needing to see the raw data.
Protection of sensitive datasets – no leaks of private or regulated info.
Trust in multi-institution AI collaboration – multiple stakeholders can safely build shared AI models.
Why this matters 👇
In healthcare, hospitals could train a shared cancer detection model, each contributing their private data, without ever exposing it.
In finance, banks could build a joint fraud detection engine, while still keeping user data fully private.
In government, regulators can ensure AI systems are trained correctly without accessing confidential datasets.
This is privacy-preserving AI at scale.
It’s not about hiding data, it’s about proving correctness in a cryptographic, transparent, and secure way.
#Lagrange DeepProve roadmap is paving the path for AI that is both trustworthy and verifiable — the foundation for AI in sensitive industries.
AI needs transparency. ZK Proofs of Training might just be the key.