Zama
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Sentiment Analysis on Encrypted Data with Homomorphic Encryption
This Hugging Face blog post demonstrates running sentiment analysis on fully homomorphic encrypted (FHE) data, enabling inference without the server ever seeing plaintext inputs. The approach combines a fine-tuned NLP model with Concrete-ML, a library that compiles ML models to FHE circuits. This represents a practical demonstration of privacy-preserving ML inference at the application layer.
Running Privacy-Preserving Inferences on Hugging Face Endpoints
Hugging Face has published a blog post describing the integration of Fully Homomorphic Encryption (FHE) with its Inference Endpoints service, enabling privacy-preserving ML inference where data remains encrypted throughout computation. The approach allows clients to send encrypted inputs to a hosted model without the server ever seeing plaintext data. This represents a practical deployment of FHE-based ML, a technique that has historically been too slow for production use but is gaining traction with recent optimizations.