Sentiment Analysis on Encrypted Data with Concrete-ML FHE and XGBoost

AI Impact Summary

The post demonstrates a complete privacy-preserving sentiment-analysis pipeline that runs the transformer-based text encoding on the client and uses a fully homomorphic encryption (FHE) model via Concrete-ML for encrypted inference with XGBoost. It leverages a Hugging Face transformer (cardiffnlp/twitter-roberta-base-sentiment-latest) to generate a 768-dim hidden representation, then trains a Concrete-ML-enabled XGBoost classifier, with deployment possible via a client/server protocol and Hugging Face Spaces demo. This approach minimizes raw text exposure to cloud services but adds architectural and performance overhead, including client-side feature extraction, FHE key management, and latency considerations for production workloads.