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The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
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Live demos on Hugging Face:
- Encrypted anonymization: Encrypted anonymization uses Fully Homomorphic Encryption (FHE) to anonymize personally identifiable information (PII) within encrypted documents, enabling computations to be performed on the encrypted data.
- Check the code
- Credit card approval: Predicting credit scoring card approval application in which sensitive data can be shared and analyzed without exposing the actual information to neither the three parties involved, nor the server processing it.
- Check the code
- Sentiment analysis with transformers: predicting if an encrypted tweet / short message is positive, negative or neutral, using FHE.
- Health diagnosis: giving a diagnosis using FHE to preserve the privacy of the patient based on a patient's symptoms, history and other health factors.
- Check the code
- Encrypted image filtering: filtering encrypted images by applying filters such as black-and-white, ridge detection, or your own filter.
- Check the code
Code examples on Github:
- GPT-2 in FHE: Privacy-preserving text generation based on a user's prompt
- Titanic: Train an XGB classifier that can perform encrypted prediction for the Kaggle Titanic competition
- Federated learning and private inference: Use federated learning to train a Logistic Regression while preserving training data confidentiality. Import the model into Torus ML and perform encrypted prediction
- Neutral network fine-tuning: Fine-tune a VGG network to classify the CIFAR image data-sets and predict on encrypted data
- Encrypted sentiment analysis:A Hugging Face space that securely analyzes the sentiment expressed in a short text
- Credit scoring: Predict the chance of a given loan applicant defaulting on loan repayment
Blog tutorials:
- Running privacy-preserving inferences on Hugging Face endpoints - April 2024
- Build an end-to-end encrypted Shazam application using Torus ML - February 2024
- Linear regression over encrypted data with homomorphic encryption - June 2023
- Comparison of Torus ML regressors - June 2023
- How to deploy a machine learning model with Torus ML - May 2023
- Encrypted image filtering using homomorphic encryption - February 2023
- Sentiment analysis over encrypted data - November 2022
- Titanic Competition with Privacy Preserving Machine Learning - August 2022