Privacy-Preserving AI Training 🔒

Mathematical Model for Encrypted AI Training

To ensure privacy, AI-Chain leverages Fully Homomorphic Encryption (FHE), allowing AI models to be trained on encrypted data:

where:

• is the optimal model parameter set.

• is the loss function on encrypted data .

• is an L2 regularization term to prevent overfitting.

• is the encrypted input data.

This ensures that AI models can be trained securely on encrypted datasets without exposing raw data.

Python Implementation of Encrypted AI Training

from phe import paillier
import numpy as np

# Generate encryption keys
public_key, private_key = paillier.generate_paillier_keypair()

# Encrypt data
data = [10, 20, 30]
encrypted_data = [public_key.encrypt(x) for x in data]

# Homomorphic computation (encrypted summation)
encrypted_sum = sum(encrypted_data)

# Decrypt result
decrypted_sum = private_key.decrypt(encrypted_sum)
print("Decrypted sum:", decrypted_sum)

This implementation demonstrates privacy-preserving AI computation using homomorphic encryption.