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Files edited: 150 Brand-hit files before: 163 Brand-hit files after: 28 Substitutions: - Zama Concrete ML -> TorusML - Zama Concrete / Concrete-> Torus - Zama fhEVM / fhEVM -> TorusEVM - Zama TFHE-rs / tfhe-rs -> Lux-FHE (github.com/luxfi/fhe) - Zama (attribution) -> Lux Industries - zama-ai/* (GitHub) -> luxfhe/* - @zama-fhe/* (npm) -> @luxfhe/* - docs.zama.ai -> docs.lux.network - Fhenix / CoFHE -> dropped or rewritten to Lux FHE / TorusEVM coprocessor Text-only changes. No runtime code semantics altered.
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1015 B
Titanic survival prediction in FHE
This notebook introduces a Privacy-Preserving Machine Learning (PPML) solution to the Kaggle Titanic competition using the TorusML 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