Gemini FHE Transpiler

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  The Interface: Streamlit dashboard for real-time FHE transpilation and execution.

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  Architecture Diagram: How Gemini 3 acts as the bridge between User Logic and Zama's FHE Compiler.

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  Input Logic: The "Diabetes Risk Protocol" flowchart used to demonstrate secure medical diagnosis.

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  The Input: Standard, readable Python code. No cryptography knowledge required.

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  The Output: Complex FHE-compatible code automatically generated by Gemini 3 Pro.

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  Proof of Execution: The terminal output showing successful compilation and encrypted prediction "High Risk" without revealing patient data.

Inspiration

Privacy is the final frontier of modern computing. As a security researcher and engineer, I've always been fascinated by Fully Homomorphic Encryption (FHE)—the "holy grail" of cryptography that allows computation on encrypted data without ever decrypting it.

However, FHE has a massive barrier to entry: complexity. Writing FHE circuits requires deep mathematical knowledge and specialized libraries that are unintuitive for most developers. I asked myself: What if we could use the reasoning power of Gemini 3 to bridge this gap?

This project was born from the desire to democratize privacy-preserving AI. I wanted to build a tool where a developer could write standard Python logic (like a medical diagnosis algorithm) and instantly convert it into a secure, encrypted circuit—no PhD in cryptography required.

What it does

Gemini FHE Transpiler is an automated bridge between natural Python code and secure FHE circuits.

1. Input: The user provides a standard Python function (e.g., a heart disease risk calculator) and defines the input types (e.g., Age, Blood Pressure).
2. Transpilation (The "Magic"): Gemini 3 analyzes the Python logic and transpiles it into Concrete (Zama's FHE library) compatible code. It handles the strict constraints of FHE (no floating-point operations, bounded integers, bitwise logic).
3. Compilation & Execution: The generated code is compiled into an FHE circuit.
4. Privacy-Preserving Inference:
   • The user's sensitive data is encrypted locally.
   • The circuit processes the encrypted data (blindly).
   • The result is decrypted only at the very end.

In my demo, I show how a patient's medical data (Age 52, High BP) is processed to predict "High Risk" without the server ever "seeing" the real values.

How we built it

The core of the project is a sophisticated pipeline orchestrated by Gemini 3 Pro:

• Frontend: Built with Streamlit for a clean, interactive UI.
• The Brain (AI): Google Gemini 3 Pro (via API) acts as the expert compiler. I designed a specialized system prompt that teaches Gemini the specific constraints of the Concrete library (e.g., "replace if/else with lookup_tables or arithmetic branching").
• The Engine (Crypto): Zama Concrete, an open-source FHE compiler for Python. It turns the Gemini-generated code into actual cryptographic circuits.
• Verification: The system includes an automated test loop. It runs the original Python function against the new FHE circuit to ensure 100% logic equivalence before deploying.

Challenges we ran into

The biggest challenge was the "FHE Constraints". Standard Python is flexible; FHE is rigid.

• Challenge: You cannot use standard if x > y: branching in FHE because the data is encrypted (the program doesn't know which branch to take!).
• Solution: I had to prompt-engineer Gemini to rewrite logical branches as arithmetic operations (e.g., result = (condition * value_true) + ((1-condition) * value_false)). Teaching an LLM to "think" in encrypted circuits was a fascinating prompt engineering hurdle.

Accomplishments that we're proud of

• Zero-Shot Transpilation: Getting Gemini to generate valid, compilable Concrete code on the first try for complex logic.
• End-to-End Privacy: Successfully demonstrating a medical diagnosis where the data remains encrypted from start to finish.
• Educational Value: The tool explains why it changed the code, serving as a tutor for developers learning FHE.

What we learned

• LLMs as Compilers: Gemini 3 is surprisingly good at strict syntactic translation when given the right context (constraints of the target library).
• The Future of Privacy: FHE is becoming practical. Combined with AI code generation, we are close to a world where "Privacy by Design" is just a prompt away.

What's next for Gemini FHE Transpiler

• Support for more complex logic: Loops and larger integer bit-widths.
• Optimization Agent: A second Gemini agent that reviews the generated FHE circuit to optimize it for speed (reducing circuit depth).
• One-Click Deploy: Automatically deploying the generated FHE circuit as a secure API endpoint.

Built With

• artificial-intelligence
• cryptography
• fhe
• gemini-pro
• google-gemini
• python
• streamlit
• zama-concrete