CHLOM™ Future-Proof AI & ML Security Framework
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Advanced Cryptographic and AI Security for Decentralized Licensing, Fraud Detection, and Automated Compliance
Version: 2.0 | Last Updated: February 2025
1. Introduction
The CHLOM™ AI & ML Security Framework is a next-generation security architecture designed to provide trustless, autonomous, and resilient machine learning systems for fraud prevention, decentralized governance, compliance automation, and AI-powered risk assessment.
CHLOM™ integrates Zero-Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Federated Learning (FL), Secure Multi-Party Computation (SMPC), and Adversarial ML Defense Mechanisms to create an AI ecosystem that is both highly secure and future-proof.
2. CHLOM™ AI Security Components
2.1 Zero-Knowledge Proof (ZKP) Authentication
- Ensures private, verifiable machine learning inference without exposing data.
- Validates AI model predictions without revealing the underlying transaction data.
- Prevents model inversion attacks that attempt to reconstruct training data.
ZKP-Based Secure AI Verification
import py_ecc.bn128 as bn128
class CHLOMZKP:
"""
CHLOM™ Zero-Knowledge Proof (ZKP) for AI Security.
Uses zk-SNARKs to verify AI model outputs without revealing private data.
"""
def __init__(self):
self.secret_key = None
def generate_proof(self, secret_key):
"""Generate a cryptographic proof for secure AI inference."""
self.secret_key = secret_key
return bn128.multiply(bn128.G1, secret_key)
def verify_proof(self, proof):
"""Verify the ZKP-based proof to ensure AI model integrity."""
return bn128.pairing(proof, bn128.G2)
2.2 Homomorphic Encryption (HE) for AI Model Privacy
- Enables AI models to process encrypted data without decryption.
- Prevents model poisoning attacks by securing training data.
- Supports secure federated learning without exposing sensitive data.
Homomorphic Encryption for AI Model Processing
from phe import paillier
class CHLOMHomomorphicAI:
"""
CHLOM™ Homomorphic Encryption for Secure AI Computation.
Encrypts AI model training and inference to prevent data leaks.
"""
def __init__(self):
self.public_key, self.private_key = paillier.generate_paillier_keypair()
def encrypt_data(self, value):
"""Encrypt data before AI model processing."""
return self.public_key.encrypt(value)
def decrypt_data(self, encrypted_value):
"""Decrypt AI model output securely."""
return self.private_key.decrypt(encrypted_value)
2.3 Federated Learning (FL) for Decentralized AI Training
- Enables secure AI training across multiple nodes without sharing raw data.
- Prevents centralized AI model compromise by decentralizing learning.
- Uses differential privacy to mask user data during AI training.
Federated Learning for Secure AI Model Training
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
class CHLOMFederatedAI:
"""
CHLOM™ Federated Learning for Secure AI Training.
Trains AI models across decentralized nodes without sharing raw data.
"""
def __init__(self):
self.model = GradientBoostingClassifier(n_estimators=200)
def train_locally(self, X, y):
"""Train AI model locally without centralizing data."""
self.model.fit(X, y)
def aggregate_models(self, global_model, local_updates):
"""Aggregate decentralized AI models securely."""
global_model.update(local_updates)
return global_model
2.4 Secure Multi-Party Computation (SMPC) for AI Training
- Allows multiple parties to train AI models without exposing private data.
- Prevents adversarial inference by splitting data between computation nodes.
- Uses threshold cryptography to ensure model security.
Secure AI Training with Multi-Party Computation
import numpy as np
import secretsharing as sss
class CHLOMSecureAITraining:
"""
CHLOM™ Secure Multi-Party Computation for AI Training.
Uses threshold cryptography to distribute AI training across multiple nodes.
"""
def __init__(self):
self.model_weights = {}
def split_model(self, model_parameters):
"""Split AI model parameters into secret shares for security."""
return sss.SecretSharer.split_secret(str(model_parameters), 3, 5)
def reconstruct_model(self, shares):
"""Reconstruct AI model parameters from secret shares."""
return sss.SecretSharer.recover_secret(shares)
2.5 Adversarial ML Defense for AI Robustness
- Defends AI models against adversarial attacks (e.g., evasion and poisoning).
- Uses adversarial training to improve AI model resilience.
- Implements gradient masking to prevent AI model exploitation.
Adversarial ML Defense in AI Training
import numpy as np
from sklearn.neural_network import MLPClassifier
class CHLOMAdversarialDefense:
"""
CHLOM™ Adversarial Defense for AI Models.
Uses adversarial training to enhance AI security against evasion attacks.
"""
def __init__(self):
self.model = MLPClassifier(hidden_layer_sizes=(128, 64))
def train_with_adversarial_samples(self, X, y):
"""Enhance AI model robustness by introducing adversarial samples."""
X_augmented = np.vstack([X, X + np.random.uniform(-0.1, 0.1, X.shape)])
y_augmented = np.hstack([y, y])
self.model.fit(X_augmented, y_augmented)
def detect_adversarial_samples(self, X):
"""Identify adversarial samples using anomaly detection."""
return np.abs(self.model.predict_proba(X)[:, 1] - 0.5) > 0.4
3. Conclusion
The CHLOM™ AI & ML Security Framework provides a next-generation security architecture for fraud prevention, decentralized AI training, and cryptographically secure model inference.
By integrating Zero-Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Federated Learning (FL), Secure Multi-Party Computation (SMPC), and Adversarial ML Defense, CHLOM™ ensures that AI models remain secure, scalable, and resistant to evolving security threats.
This framework represents a future-proof AI governance model, enabling trustless and decentralized AI decision-making while maintaining robust security and compliance enforcement.
