pamola-core 0.0.1

PAMOLA Core — Privacy-Aware Management of Large Anonymization. Foundational privacy engineering library.

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.

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

pip install pamola-core

License

Proprietary — REALM Inveo Inc. All rights reserved.

Contact

• Website: realmdata.io
• Email: [e-mail]
• Organization: REALM Inveo Inc.