Metadata-Version: 2.4
Name: pamola-core
Version: 0.0.1
Summary: PAMOLA Core — Privacy-Aware Management of Large Anonymization. Foundational privacy engineering library.
Author-email: "REALM Inveo Inc." <info@realminveo.com>
Maintainer-email: "REALM Inveo Inc." <info@realminveo.com>
License-Expression: LicenseRef-Proprietary
Project-URL: Homepage, https://realmdata.io
Project-URL: Documentation, https://docs.realmdata.io
Project-URL: Repository, https://github.com/realminveo/pamola-core
Project-URL: Bug Tracker, https://github.com/realminveo/pamola-core/issues
Project-URL: Changelog, https://github.com/realminveo/pamola-core/blob/main/CHANGELOG.md
Keywords: privacy,anonymization,data-privacy,differential-privacy,k-anonymity,pii,gdpr,data-protection,synthetic-data,federated-learning
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# 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/)
