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Securing your experience...
Securing your experience...
Train machine learning models on encrypted datasets. Computation is accurate. Sensitivity is preserved. Data owner retains the encryption key.
You have sensitive datasets (customer data, proprietary telemetry, genomics) and you want to train ML models on them. But you don't trust the cloud. You can't decrypt data in the cloud. You need to train on plaintext to get accurate models.
Encrypt your dataset with your key. Send ciphertext to cloud.
Train supervised ML models on encrypted features. Supported: logistic regression, linear classifiers, tree-based models, kernel methods.
Model training is accurate—results are identical to plaintext training.
Get trained model back encrypted. Decrypt with your key. Model is yours.
Dataset owner encrypts data with FHE key → uploads ciphertext to cloud → cloud trains model on ciphertext → training loss and accuracy computed on encrypted features → trained model weights are encrypted → owner downloads and decrypts → model is ready for inference.
Cloud never sees plaintext data or features. Only encrypted data and encrypted model weights. Training accuracy is mathematically identical to plaintext training. No approximations, no accuracy loss.
Data flows encrypted from ingestion to training. Cloud never holds plaintext. Only encrypted ciphertext and encrypted weights are stored.
No approximations. Training loss and model weights are computed as if training on plaintext. Accuracy is mathematically identical.
Logistic regression, linear classifiers, decision trees, random forests, kernel methods. More algorithms coming as FHE optimization improves.
You control the encryption key. Cloud is untrusted. Trained model is encrypted in cloud; only you can decrypt it.