BlindML
scikit-learn for Encrypted Data
BlindML trains Naive Bayes, decision trees, histogram and logistic regression models run on encrypted records in seconds. Zero plaintext exposure. Your machine learning workflows stay intact, your models get the data they need without violating compliance or security policies.
Trained on aggregate counts, not plaintext records
Marginals are cross-tabulated count summaries over encrypted fields. The Blind Insight platform computes them by issuing aggregate queries against ciphertext and returning a count table. The model trains on that table—never on feature vectors derived from decrypted records.
from blind_ml import NaiveBayesModel
model = NaiveBayesModel().fit(marginals, n_pos=3201, n_neg=76402)
pred, risk = model.predict({"fraud_type": "card_fraud"})
# 0.0 F1 delta vs. plaintext — 600K recordsblind grants create --data '{
"name": "blindml-training",
"field_names": {"risk_level": true, "fraud_type": true, "is_fraud": true},
"can_create_records": false
}'Supported algorithms
Source of truth: blind-insight/blind-ml on GitHub — open source.
- Naive Bayes Best for multi-class classification tasks over categorical fields with conditional independence assumptions.
- Decision Trees Interpretable split-based models; useful when explainability or auditable decision paths are required.
- Logistic Regression Linear decision boundaries; calibrated probabilities for binary classification over structured aggregate features.
The numbers.
- 0.0 F1 delta
- vs. plaintext baseline, 600K records
- HIPAA k=11
- Suppression built in—F1 holds
- Naive Bayes · Decision Trees · Logistic Regression
- scikit-learn compatible
- No plaintext exposure
- Training and inference on aggregates only
Start building on your schema.
Get started at the Build tier, $9/month.