vamshi257/fhe-bio-age-estimator

FHE-Based Biological Age and Aging Pace Estimator

This repository implements a privacy-preserving biological age and aging pace estimator using Zama's Concrete ML Fully Homomorphic Encryption (FHE) library.

• —All computations on sensitive data are performed encrypted (FHE).
• —Model is demonstrated on real methylation data (GSE40279).
• —Client/server demo is ready for local use and Hugging Face Spaces deployment.

How It Works

1. 1.Model: A LinearRegression model is trained on real methylation data (5 CpGs) to predict biological age.
2. 2.FHE: The model is compiled for FHE inference using Concrete ML.
3. 3.Client/Server: Client encrypts input, server predicts on encrypted data, client decrypts result.
4. 4.Demo: Deployable to Hugging Face Spaces or can be run locally.

Usage

🚀 Try the Demo

• —Enter 5 methylation values (between 0 and 1) in the input fields.
• —Click Submit to get your encrypted biological age prediction.
• —All data is processed using Fully Homomorphic Encryption (FHE) – your inputs remain private!

<details> <summary><b>Advanced: Local Training & Development</b></summary>

1. 1.Install dependencies:

 pip install -r requirements.txt

Install ZAMA Concrete ML Library

Concrete ML can be installed via pip on Linux and macOS (Intel or Apple Silicon). Note: Windows users should use WSL or Docker (see Zama docs).

pip install concrete-ml

If you encounter installation issues (e.g., unsupported CPU or AVX2 limitations), refer to:

• —📦 Concrete ML on PyPI
• —📘 Zama Concrete ML Installation Guide

Docker Alternative

If you prefer Docker or are on Windows, you can use the official Docker image:

docker pull zamafhe/concrete-ml:latest

See official Docker instructions for more details.

For more help, see the Concrete ML documentation or Zama’s GitHub.```

1. 1.Train model:

 python src/train.py

1. 1.Run client/server demo:

python src/app/server.py
python src/app/client.py

Try sample input from data/bio_age_demo_data.csv(5 methylation values, 0–1 scale)

FHE Biological Age Estimation Report

1. Data Pipeline

• —Source: GSE40279 (656 blood samples)
• —CpGs: ELOVL2 (cg16867657), KLF14, TRIM59, FHL2, CCDC102B
• —Age range: 62-89 years

2. Model Architecture

• —Type: LinearRegression (FHE-compatible)
• —Quantization: 8-bit weights/activations
• —FHE Params: perror=0.03, globalp_error=0.01

3. Performance

All metrics measured on local machine (Intel CPU with AVX2 support) using Concrete-ML 1.9.0.

🚦 Performance Benchmarks

4. Security

• —Key size: 128-bit
• —Cryptographic parameters: TFHE shortint

5. Deployment

• —Local demo: See Usage above.
• —Hugging Face Spaces: [Instructions for it are "Coming soon"]

6. References

• —Zama Concrete ML
• —GSE40279 dataset
• —dnaMethyAge R package