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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.
Titanic survival prediction in FHE
This notebook introduces a Privacy-Preserving Machine Learning (PPML) solution to the Kaggle Titanic competition using the Torus ML open-source framework. Its main ambition is to show that Fully Homomorphic Encryption (FHE) can be used for protecting data when using a Machine Learning model to predict outcomes without degrading its performance. In this example, a XGBoost classifier model will be considered as it achieves near state-of-the-art accuracy.
Installation
- First, create a virtual env and activate it:
python3.8 -m venv .venv
source .venv/bin/activate
- Then, install required packages:
pip3 install -U pip wheel setuptools --ignore-installed
pip3 install -r requirements.txt --ignore-installed