Applied Privacy Lite is an open-source initiative dedicated to fortifying data privacy through a two-phase approach. The first phase involves implementing a robust privacy layer using advanced transformers in natural language processing (NLP) for the identification and extraction of Personally Identifiable Information (PII) from textual data. In the second phase, the focus shifts to incorporating federated learning, allowing the model to learn across decentralized devices while prioritizing user privacy.

• PII Identification: Employ cutting-edge transformers in NLP for the effective identification and extraction of PIIs from textual data.
• Anonymization: Implement mechanisms to anonymize sensitive information, ensuring the privacy of user data.

• Decentralized Learning: Enable federated learning to train models across distributed devices without compromising user data.
• Privacy-Preserving Training: Implement techniques to maintain the privacy of individual data during the federated learning process.

• Python 3.x
• [To be updated soon...]

1. Clone the repository:

   git clone https://github.com/your-username/AppliedPrivacyLite.git

2. Install dependencies:

   [To be updated soon...]

[To be updated soon...]

We welcome contributions to Applied Privacy Lite! To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your-feature.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature/your-feature.
5. Open a pull request.

• Thanking Rom, Koki and Michael for their support in this enterprise.