https://github.com/x0prc/qrep
Quantum Resistant Engine for Privacy
https://github.com/x0prc/qrep
custom-assessment-tool cybersecurity privacy quantum-computing
Last synced: 7 days ago
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Quantum Resistant Engine for Privacy
- Host: GitHub
- URL: https://github.com/x0prc/qrep
- Owner: x0prc
- License: mit
- Created: 2025-04-03T05:40:48.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-12T17:03:50.000Z (5 months ago)
- Last Synced: 2025-09-12T19:50:07.028Z (5 months ago)
- Topics: custom-assessment-tool, cybersecurity, privacy, quantum-computing
- Language: Python
- Homepage:
- Size: 54.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: .github/README.md
- License: LICENSE
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README
## Quantum Resistant Engine for Privacy
Custom Assessment Tool for Security Testing and Research
[](https://github.com/x0prc/QREP/actions/workflows/main.yml)
## 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.
---
## Architecture
### Layers:
1. **Preprocessing Layer**: Quantum-Sealed Tokenization
2. **Core Anonymization Layer**: Context-Aware Differential Privacy
3. **Post-Processing Layer**: Homomorphic Masking
---
## Installation
### Prerequisites
- Python 3.9+
- Libraries:
- `cryptography`
- `pqcrypto`
- `transformers`
- `tenseal`
- Docker (for deployment)
- AWS Nitro Enclaves (optional for sensitive operations)
---
### 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`
---
### Dataset Instructions
[Kaggle Credit Card Fraud Detection](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
⚠️ 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.
---
## 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 [c0ldheat@protonmail.com].