QREP

Repository: https://github.com/x0prc/QREP

A Comprehensive Technical Guide

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Table of Contents

1. Introduction
2. Core Features
3. Module Deep Dives
4. Code Examples
5. Compliance Framework
6. Testing

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Introduction

QREP (Quantum Resistant Engine for Privacy) is a cutting-edge privacy-preserving toolkit designed for the post-quantum cryptography era. It provides multi-layer data protection combining lattice-based cryptographic techniques, behavioral biometrics, differential privacy, and federated learning.

This project addresses the growing need for quantum-resistant security measures while maintaining regulatory compliance across GDPR, CCPA, and HIPAA frameworks.

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Core Features

1. Quantum-Sealed Tokenization

• BLAKE2s cryptographic hashing for quantum resistance
• Behavioral biometric sealing via keystroke/mouse dynamics
• Automatic key rotation at configurable intervals
• Token verification for data integrity

2. Context-Aware Differential Privacy

• AI-driven privacy budget calculation using transformer models
• Dynamic epsilon adjustment based on data sensitivity
• Laplace noise injection for differential privacy guarantees
• Synthetic data generation for high-risk scenarios

3. Homomorphic Masking

• Secure computations on encrypted data
• GAN-based synthetic data generation for data augmentation
• Federated learning support for distributed training

4. Compliance Assurance Module

Automated regulatory adherence for:

Regulation	Technique	Verification
GDPR	Article 25 Pseudonymization	ZKP Proof Generation
CCPA	§1798.140(o) De-Identification	Blockchain Auditing
HIPAA	Safe Harbor Expert Determination	Federated Learning Checks

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Module Deep Dives

1. Quantum Tokenizer (src/tokenization/quantum_tokenizer.py)

The QuantumTokenizer class implements quantum-resistant tokenization using BLAKE2s hashing combined with behavioral biometrics.

Key Methods:

• update_biometric_pattern(keystroke_timings, mouse_trajectory) - Captures behavioral patterns
• generate_token(data) - Creates quantum-sealed tokens
• verify_token(token, data) - Verifies token integrity
• _rotate_keys_if_needed() - Automatic key rotation

Configuration:

tokenizer = QuantumTokenizer(key_rotation_interval=86400)  # 24 hours

2. Biometric Capture (src/tokenization/biometric_capture.py)

The BiometricCapture class captures behavioral biometrics using keyboard and mouse listeners.

Features:

• Keystroke timing capture (inter-key intervals)
• Mouse trajectory tracking (position + timestamps)
• Click event capture with button identification
• Configurable capture duration

Usage:

capture = BiometricCapture()
data = capture.capture(duration=10)  # Capture for 10 seconds

3. Context-Aware Differential Privacy (src/differential/differential_privacy.py)

The ContextAwareDP class implements AI-enhanced differential privacy with dynamic budget allocation.

Key Methods:

• calculate_privacy_budget(context_score, diversity_metric) - Computes epsilon
• add_laplace_noise(data, sensitivity, epsilon) - Adds DP noise
• apply_differential_privacy(data, epsilon) - Main DP application
• process_data(text_data, diversity_metric) - Full pipeline with AI analysis

Privacy Budget Calculation:

epsilon = self.epsilon_base * context_score * (1 + diversity_metric / 10)

4. GAN Manager (src/differential/gan_manager.py)

The GANManager and StyleGANTrainer classes handle synthetic data generation.

Features:

• Checkpoint versioning and management
• Sample image generation with metadata
• Training metrics logging (including FID scores)
• Model state serialization

Training Configuration:

trainer = StyleGANTrainer(data_path="./data")
config = trainer.train(num_epochs=100, batch_size=8, lr=0.002)

5. Federated Learning (src/differential/federated_learning.py)

The FederatedTrainer class implements privacy-preserving distributed training using PySyft.

Features:

• Virtual worker creation for federated nodes
• Secure model aggregation via federated averaging
• Integration with HuggingFace transformers
• Differential privacy in training loop

Workflow:

trainer = FederatedTrainer(model, data_shards, num_rounds=3)
model = trainer.train()

6. Financial Transactions Dataset (src/differential/financial_transactions_dataset.py)

The FinancialTransactionsDataset class provides data processing utilities.

Features:

• CSV data loading
• Feature normalization
• Laplace noise injection
• Data sharding for federated learning
• Synthetic data generation

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Code Examples

Complete Tokenization Workflow

from src.tokenization.quantum_tokenizer import QuantumTokenizer
from src.tokenization.biometric_capture import BiometricCapture
 
# Step 1: Capture biometric data
capture = BiometricCapture()
biometric_data = capture.capture(duration=10)
 
# Step 2: Initialize tokenizer
tokenizer = QuantumTokenizer(key_rotation_interval=86400)
 
# Step 3: Update with biometric pattern
tokenizer.update_biometric_pattern(
    biometric_data["keystroke"],
    biometric_data["mouse"]
)
 
# Step 4: Generate token
data = b"Sensitive financial data"
token = tokenizer.generate_token(data)
 
# Step 5: Verify token
is_valid = tokenizer.verify_token(token, data)
print(f"Token valid: {is_valid}")

Applying Differential Privacy

import numpy as np
from src.differential.differential_privacy import ContextAwareDP
 
# Initialize DP with base epsilon
dp = ContextAwareDP(epsilon_base=1.0)
 
# Calculate dynamic privacy budget
epsilon = dp.calculate_privacy_budget(
    context_score=7,
    diversity_metric=4.2
)
print(f"Adjusted epsilon: {epsilon}")
 
# Apply DP to data
data = np.array([100.0, 200.0, 50.0, 75.0])
noisy_data = dp.apply_differential_privacy(data, epsilon=epsilon)
print(f"Original: {data}")
print(f"With noise: {noisy_data}")

Training a GAN

from src.differential.gan_manager import StyleGANTrainer
 
# Initialize trainer
trainer = StyleGANTrainer(
    data_path="./data/financial_transactions",
    results_dir="./gan_results",
    models_dir="./gan_models"
)
 
# Train model
config = trainer.train(
    num_epochs=100,
    batch_size=8,
    lr=0.002
)
 
print(f"Training complete. Model saved with config: {config}")

Federated Learning Setup

import torch
import torch.nn as nn
from src.differential.federated_learning import FederatedTrainer
from transformers import AutoModelForSequenceClassification
 
# Create model
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
 
# Split data into shards (one per worker)
data_shards = [
    [{"text": "sample1", "label": 1}, {"text": "sample2", "label": 0}],
    [{"text": "sample3", "label": 1}, {"text": "sample4", "label": 1}]
]
 
# Initialize federated trainer
trainer = FederatedTrainer(
    model=model,
    data_shards=data_shards,
    num_rounds=3
)
 
# Train across workers
trained_model = trainer.train()

Financial Data Processing

from src.differential.financial_transactions_dataset import FinancialTransactionsDataset
 
# Load dataset
dataset = FinancialTransactionsDataset(
    file_path="./data/financial_transactions/transactions.csv"
)
data = dataset.load_data()
print(f"Loaded {len(data)} transactions")
 
# Preprocess
features, labels = dataset.preprocess()
print(f"Features shape: {features.shape}")
 
# Apply Laplace noise (DP)
noisy_features = dataset.add_laplace_noise(epsilon=0.1)
 
# Split for federated learning
shards = dataset.split_into_shards(num_shards=4)
print(f"Created {len(shards)} data shards")
 
# Generate synthetic data
synthetic = dataset.generate_synthetic_data(num_samples=1000)
print(f"Generated {len(synthetic)} synthetic records")

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Testing

Run All Tests

pytest tests/

Test Coverage

Module	Test File	Coverage
Tokenizer	tests/test_tokenizer.py	Token generation, verification, key rotation
Differential Privacy	tests/test_DP.py	Privacy budget, noise injection, synthetic data
GAN Manager	tests/test_GM.py	Checkpoints, model loading, metadata

Sample Test Output

$ pytest tests/test_tokenizer.py -v
 
test_biometric_pattern_update PASSED
test_generate_and_verify_token PASSED
test_key_rotation PASSED

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Compliance Framework

GDPR (General Data Protection Regulation)

• Article 25: Data protection by design and default
• Technique: Pseudonymization via quantum-sealed tokens
• Verification: Zero-Knowledge Proof (ZKP) generation

CCPA (California Consumer Privacy Act)

• §1798.140(o): De-identification definition
• Technique: Differential privacy with dynamic budgets
• Verification: Blockchain-based audit trails

HIPAA (Health Insurance Portability and Accountability Act)

• Safe Harbor: Expert determination method
• Technique: Federated learning for distributed analysis
• Verification: Privacy budget validation at each node

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Dependencies

Category	Package	Purpose
Cryptography	cryptography	BLAKE2s hashing
Cryptography	pqcrypto	Post-quantum algorithms
ML Framework	torch	Neural networks
Transformers	transformers	NLP for context analysis
GAN	stylegan2_pytorch	Synthetic data generation
Federated	syft	Privacy-preserving ML
Data	pandas, numpy	Data processing
Testing	pytest	Unit testing

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Conclusion

QREP provides a comprehensive, production-ready solution for quantum-resistant privacy preservation. Its modular architecture allows for flexible deployment across various regulatory environments while maintaining strong security guarantees.

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