Opacus
This entry runs no foundation model. Every finding below is quoted to the vendor’s own document, or marked not disclosed where the vendor publishes nothing.
PyTorch library for training models with differential privacy (DP-SGD) — injects calibrated noise into gradients to prevent membership inference attacks on sensitive training data
No foundation model. Opacus does not run one, so this criterion does not apply and is excluded from the grade rather than counted against it. A Meta/PyTorch library that adds differential privacy to training by wrapping the user's own optimizer and data loader with DP-SGD gradient clipping and noise; it trains your model and provides none of its own. Established from the product’s own public documentation and what it does. If that is out of date, tell us at right of reply.
Not yet assessed. We publish a sovereignty position only where the vendor documents one — we do not infer it from a domain or a company name.
Taken from the vendor’s own published material. Vannus does not hold these reports and has not reviewed their scope or dates — ask the vendor for the current report before relying on any of them.
Vannus records, from the vendor's own published documents, the legal entity a customer contracts with, the country that entity sits in, and the governing law of its terms — and whether the vendor runs its own model or resells someone else's. Each finding is quoted to its source and dated, or marked not disclosed where the vendor publishes nothing. No paid placements — affiliate status is walled off from the record, enforced by a test in the build. See the methodology →