As a data scientist, you're probably used to this tradeoff: Insight vs. Privacy. Imagine analyzing data without actually seeing it. This is where Fully Homomorphic Encryption (FHE) comes in. Fully Homomorphic Encryption (FHE) lets you run computations on encrypted data. You never decrypt and you still get useful results. Imagine running an ML model on patient data, financial data or user input—without ever accessing the raw data. @zama is leading the charge to make FHE practical for ML: Concrete ML: A framework to do encrypted inference using familiar tools (scikit-learn, PyTorch) works on tabular, regression, and even small neural nets, it runs locally or remotely and still encrypted. You can deploy a model on sensitive health records with no data leakage or run personalized recommendations without ever seeing user data. Privacy is no longer a blocker to insights, it’s built in.
- 0 replies
- 0 recasts
- 0 reactions
Happy Thursday to all
- 0 replies
- 0 recasts
- 0 reactions
Claim your $pstack https://farpower.vercel.app/?ref=568552
- 0 replies
- 0 recasts
- 0 reactions