This blog post introduces a Privacy-Preserving Machine Learning (PPML) solution to the Titanic challenge found on Kaggle using the Concrete-ML open-source toolkit. 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, an XGBoost classifier model will be considered as it achieves near state-of-the-art accuracy.
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