Training Predictive Models on Encrypted Data using Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a transformative technology that enables the processing of encrypted data without compromising its security, presenting significant implications for sectors such as healthcare, finance, and genomics. This technology allows for the training of machine learning models on encrypted data, fostering privacy and collaboration by enabling entities to enhance their models with external data while maintaining confidentiality. The Concrete ML framework exemplifies the application of FHE by supporting the training of Logistic Regression models on encrypted data, using stochastic gradient descent to ensure model robustness and interpretability. The current approach integrates encryption, training, and decryption, which facilitates the exploration of model accuracy in development settings, with plans to extend its application to client-server environments and more complex models in the future. This advancement not only safeguards privacy but also promotes trust and cooperation, paving the way for innovative, secure collaborations across industries.