# PAMOLA Core

**Privacy-Aware Management of Large Anonymization**

PAMOLA Core is the foundational privacy engineering library of the PAMOLA platform, developed by [REALM Inveo Inc.](https://realmdata.io)

## Overview

PAMOLA Core provides atomic, composable privacy-preserving operations designed to break down complex privacy processes into reusable building blocks. The platform enables users to build sophisticated privacy workflows through composition rather than monolithic functions.

### Key Capabilities

- **Anonymization** — Generalization, noise addition, suppression, masking, pseudonymization
- **Privacy Models** — k-Anonymity, l-Diversity, t-Closeness, Differential Privacy
- **Attack Simulation** — Re-identification risk assessment and linkage attacks
- **Synthetic Data** — Statistical synthetic data generation (CTAB-GAN+, PATE-GAN)
- **Fake Data** — Rule-based realistic data generation
- **Federated Learning** — Privacy-preserving distributed model training
- **Secure Computation** — Multi-party computation protocols (PSI, secret sharing)
- **Data Profiling** — Automated data analysis and classification
- **Transformation** — Data type conversion and normalization
- **Metrics** — Fidelity, utility, and privacy measurement

## Status

⚠️ **This is an initial namespace reservation release (0.0.1).** Functional packages are under active development.

## Requirements

- Python ≥ 3.10

## Installation

```bash
pip install pamola-core
```

## License

Proprietary — REALM Inveo Inc. All rights reserved.

## Contact

- **Website**: [realmdata.io](https://realmdata.io)
- **Email**: info@realminveo.com
- **Organization**: [REALM Inveo Inc.](https://pypi.org/org/realminveo/)
