Sentiment Analysis on Encrypted Data with Homomorphic Encryption

1. 1Setup the environment with Concrete-ML library
2. 2Use a public dataset for sentiment analysis
3. 3Represent text using a transformer
4. 4Classify with XGBoost
5. 5Predict over encrypted data with Concrete-ML
6. 6Deploy the model to the cloud

Data scientists and machine learning engineers can benefit from this tutorial to build privacy-preserving sentiment analysis models, while software engineers and devops teams can learn about deployment to the cloud

💡 Homomorphic encryption enables computation on encrypted data without decryption, making it suitable for privacy-sensitive applications

🔒 Perform sentiment analysis on encrypted data with Concrete-ML library! #homomorphicencryption #sentimentanalysis