Introduction
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As the use of sensitive personal data in machine learning models becomes increasingly prevalent, ensuring data privacy and security is paramount. Private-FHE-fraud-detection addresses these concerns by leveraging advanced techniques such as Privacy-Preserving Machine Learning (PPML) and Fully Homomorphic Encryption (FHE).

Main Objectives
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- **Secure Predictions**: Enable predictions on encrypted data without decryption.
- **Data Privacy**: Ensure user data remains confidential.
- **Scalability**: Implement a client-server architecture for efficient requests.

Technologies Used
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- **PPML**
- **FHE (via Concrete ML)**
- **FastAPI** for server-client interactions.
- **Sphinx** for documentation generation.