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Credit card approval: Predicting credit scoring card approval application in which sensitive data can be shared and analyzed without exposing the actual information to neither the three parties involved, nor the server processing it.
- Check the code here
Sentiment analysis with transformers: predicting if an encrypted tweet / short message is positive, negative or neutral, using FHE.
- Check the code here and the blog post
Health diagnosis: giving a diagnosis using FHE to preserve the privacy of the patient based on a patient's symptoms, history and other health factors.
- Check the code here
Encrypted image filtering: filtering encrypted images by applying filters such as black-and-white, ridge detection, or your own filter.
- Check the code here

GPT-2 in FHE: Privacy-preserving text generation based on a user's prompt
Titanic: Train an XGB classifier that can perform encrypted prediction for the Kaggle Titanic competition
Federated learning and private inference: Use federated learning to train a Logistic Regression while preserving training data confidentiality. Import the model into Concrete ML and perform encrypted prediction
Neutral network fine-tuning: Fine-tune a VGG network to classify the CIFAR image data-sets and predict on encrypted data
Encrypted sentiment analysis:A Hugging Face space that securely analyzes the sentiment expressed in a short text
Credit scoring: Predict the chance of a given loan applicant defaulting on loan repayment

Last updated 21 days ago