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 there is no model provenance to record. 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 If that is out of date, tell us at right of reply.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.
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