Quantum Resistant Engine for Privacy

Custom Assessment Tool for Security Testing and Research

Features

1. Quantum-Sealed Tokenization: Combines lattice-based cryptography with behavioral biometric hashing for robust data preprocessing.
2. Context-Aware Differential Privacy: Dynamically adjusts privacy budgets based on data sensitivity using AI-driven analysis.
3. Homomorphic Masking: Enables secure computations on encrypted data without decryption.
4. Compliance Assurance Module: Automates regulatory adherence for GDPR, CCPA, HIPAA, and more.

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Architecture

Layers:

1. Preprocessing Layer: Quantum-Sealed Tokenization
2. Core Anonymization Layer: Context-Aware Differential Privacy
3. Post-Processing Layer: Homomorphic Masking

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Installation

Prerequisites

• Python 3.9+
• Libraries:
  • cryptography
  • pqcrypto
  • transformers
  • tenseal
• Docker (for deployment)
• AWS Nitro Enclaves (optional for sensitive operations)

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Create and activate virtual environment

python -m venv venv source venv/bin/activate # Linux/macOS venv\Scripts\activate # Windows

Dependencies

pip install -r requirements.txt

Capture Biometric Data

python -m src.tokenization.biometric_capture

GAN Training

chmod +x scripts/train_gan.sh ./scripts/train_gan.sh

Deployment

./scripts/deploy.sh

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Dataset Instructions

Kaggle Credit Card Fraud Detection

⚠️ Make sure to place the transactions.csv file in the /data/financial_transactions/ directory before executing.

⚠️ You need to have an Nvidia GPU with CUDA installed.

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Compliance Assurance Module

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

Contact

For inquiries or contributions, contact [[email protected]].