https://github.com/mihaibc/privacypilot
🛡️ PrivacyPilot – A privacy-centric backend API leveraging AI (LLaMA, Mistral, Ollama) to anonymize and moderate sensitive data in real-time. Built with Go, Node.js, and Perl. GDPR-compliant and infrastructure-ready.
https://github.com/mihaibc/privacypilot
ai anonymization azure-ai backend devops gdpr go microservices nodejs ollama perl5 privacy stable-diffusion
Last synced: 6 months ago
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🛡️ PrivacyPilot – A privacy-centric backend API leveraging AI (LLaMA, Mistral, Ollama) to anonymize and moderate sensitive data in real-time. Built with Go, Node.js, and Perl. GDPR-compliant and infrastructure-ready.
- Host: GitHub
- URL: https://github.com/mihaibc/privacypilot
- Owner: mihaibc
- License: mit
- Created: 2025-03-30T13:49:28.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-30T19:54:32.000Z (6 months ago)
- Last Synced: 2025-03-30T20:24:42.074Z (6 months ago)
- Topics: ai, anonymization, azure-ai, backend, devops, gdpr, go, microservices, nodejs, ollama, perl5, privacy, stable-diffusion
- Language: Go
- Homepage:
- Size: 14.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# 🛡️ PrivacyPilot
**PrivacyPilot** is an open-source, privacy-focused backend platform designed to automatically detect, moderate, and anonymize sensitive personal information using advanced AI models. It ensures data privacy, security, and compliance with GDPR, making it ideal for developers who prioritize user privacy.
---
## 🌟 Key Features
- ✅ **Real-time Data Anonymization**: Protect user identities by anonymizing sensitive textual and visual data instantly.
- ✅ **Automated Content Moderation**: Intelligent AI-driven moderation of harmful or inappropriate content.
- ✅ **AI Integration**: Supports local (Ollama with Mistral, LLaMA) and cloud-based (Azure AI, Stable Diffusion) models.
- ✅ **Scalable Microservice Architecture**: Efficient, reliable microservices built with Go, Node.js, Perl, and Python.
- ✅ **Infrastructure & DevOps**: Containerized (Docker), orchestrated (Kubernetes), CI/CD via GitHub Actions, infrastructure managed through Terraform.
- ✅ **Privacy and Security Compliance**: GDPR-compliant, OAuth-secured APIs, secure data handling practices.
- ✅ **Observability & Metrics**: Real-time monitoring with Prometheus and Grafana dashboards.
---
## 🛠️ Tech Stack
| Category | Technologies Used |
|-------------------------|-------------------------------------------------------------|
| **Backend** | Go, Node.js, Perl, Python |
| **AI Services** | Ollama, Azure AI/OpenAI, Stable Diffusion |
| **Infrastructure** | Docker, Kubernetes, Terraform, GitHub Actions |
| **Observability** | Prometheus, Grafana |
| **Protocols & Security**| REST APIs, OAuth, GDPR-compliant data handling |
---
## 🚀 Getting Started
Follow these instructions to quickly set up PrivacyPilot locally for development or testing purposes.
### 📋 Prerequisites
- [Docker](https://docs.docker.com/get-docker/)
- [Docker Compose](https://docs.docker.com/compose/install/)
### ⚙️ Installation & Running Locally
Clone the repository:
```bash
git clone https://github.com//PrivacyPilot.git
cd PrivacyPilot/devops
docker-compose up --build
```
### 🧪 Testing the Installation
Test the health endpoint:
```bash
curl -X GET http://localhost:3000/health
```
Test anonymization API:
```bash
curl -X POST http://localhost:3000/anonymize \
-H "Content-Type: application/json" \
-d '{"text": "Sensitive data here"}'
```
---
## 📚 Project Documentation
Explore the following documents for comprehensive guidance:
- [📖 Contribution Guidelines](CONTRIBUTING.md)
- [🧑💻 Issue and PR Creation Guidelines](ISSUE_PR_GUIDELINES.md)
- [📜 Code of Conduct](CODE_OF_CONDUCT.md)
- [📝 Coding Style & Conventions](CODING_STYLE_AND_CONVENTIONS.md)
- [📄 License](LICENSE)
---
## 🤝 Contributing
Contributions to PrivacyPilot are greatly appreciated! Please follow these simple steps to contribute effectively:
1. **Fork** the repository.
2. **Create an issue** describing your intended contribution clearly.
3. **Link** your pull request to the created issue.
4. **Follow** the guidelines outlined in:
- [Contribution Guidelines](CONTRIBUTING.md)
- [Issue & PR Guidelines](ISSUE_PR_GUIDELINES.md)
---
## 🚧 Project Structure Overview
```text
PrivacyPilot/
├── backend-api/ # Backend microservices (gateway, anonymizer, moderator)
├── ai-engine/ # AI service integrations (Ollama, Azure AI, Stable Diffusion)
├── perl-utils/ # Perl scripts for log analysis & batch processing
├── devops/ # DevOps scripts & configurations (Docker, Terraform, Kubernetes)
├── observability/ # Observability tools configuration (Prometheus, Grafana)
├── docs/ # Documentation
├── CONTRIBUTING.md
├── ISSUE_PR_GUIDELINES.md
├── CODE_OF_CONDUCT.md
├── CODING_STYLE_AND_CONVENTIONS.md
├── README.md
└── LICENSE
```
---
## 📫 Contact & Support
For questions, suggestions, or to report issues, open an issue on this repository or contact me directly:
- 🐛 **Report Issues:** [Open an issue](https://github.com//PrivacyPilot/issues)
---
## ⚖️ License
PrivacyPilot is released under the [MIT License](LICENSE).
---
### 🙌 Acknowledgments
- Inspired by privacy-focused organizations like [DuckDuckGo](https://duckduckgo.com).
- Thanks to the open-source community for amazing tools and frameworks used.
---
Built with ❤️ for privacy.