Privacy-Preserving AI Training Protocol 🔒

AI-Chain leverages Zero-Knowledge Proofs (ZK-SNARKs), Fully Homomorphic Encryption (FHE), and Multi-Party Computation (MPC) to protect AI training data.

📌 Mathematical Model

Privacy-preserving AI training ensures that AI models are trained without exposing raw data:

where:

• is the AI model parameter set

• is the encrypted data input

• is the loss function on encrypted data

• is a regularization term to prevent overfitting

📌 Python Implementation

from phe import paillier
import numpy as np

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

# Encrypt data
data = [public_key.encrypt(x) for x in [5, 10, 15]]

# Compute encrypted sum
encrypted_sum = sum(data)

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