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Securing your experience...
Securing your experience...
Send your proprietary ML model encrypted to customers. They evaluate on their plaintext data locally. Model is never exposed.
You've built a proprietary ML model (fraud detection, scoring, prediction) and you want to let customers evaluate it on their own data before buying. But you can't share the model itself—competitors would reverse-engineer it. You can't ask customers to send data to you—they won't trust that.
Encrypt your model with FHE. Send encrypted model to customer. Customer evaluates model on their plaintext data locally. Model weights are never exposed. Customer sees only inference results.
Model vendor encrypts trained model weights with FHE.
Customer receives encrypted model and evaluation client.
Customer evaluates model on their plaintext data locally. Model stays encrypted.
Inference results are generated on plaintext. Model weights and internals are never exposed.
Customer sees only predictions and metrics. Model structure remains proprietary.
Model stays proprietary throughout. Customer trusts the local evaluation (it runs on their machine). Vendor protects IP. Win-win.
Model weights and structure are encrypted. Customers can't reverse-engineer or extract the model from evaluation.
Customer evaluates on their own data on their own machines. No data leaves customer environment. No model exposure.
Provide encrypted model + evaluation client. Customer runs inference locally. No special compliance or agreements needed.
Inference on encrypted model is fast enough for real-time use cases. Latency is measured in milliseconds.