PAMOLA Architecture

PAMOLA is designed as a modular and scalable privacy AI platform built on top of DataHub for dataset governance, local Keycloak for authentication, and a flexible privacy-preserving pipeline system. At its core, PAMOLA integrates a high-performance library (PAMOLA CORE), enabling efficient execution of privacy-preserving AI operations.

Overview of PAMOLA’s Architecture

PAMOLA’s architecture is designed for secure, scalable, and privacy-enhancing AI operations. It enables organizations to manage sensitive data, apply privacy-preserving techniques, and execute AI workflows in a controlled environment.

The four main architectural components include: 1. 🗂️ DataHub-Based Dataset Governance – Manages metadata, access policies, and transformations. 2. 🔐 Keycloak Authentication & Role-Based Access Control (RBAC) – Secure user management. 3. 🔄 Project & Pipeline System – Enables workflow automation for data anonymization, synthetic data generation, and federated AI. 4. 🛠️ PAMOLA CORE Library – High-performance, independent privacy-enhancing computation engine.

— ### 🗂️ DataHub-Based Dataset Governance PAMOLA is built on DataHub, a metadata-driven data catalog and governance framework. This ensures full transparency, compliance, and lifecycle tracking of datasets used in privacy-preserving AI projects.

Key features of DataHub integration:

  • Dataset Lineage & Provenance Tracking – Understand how data flows through pipelines.

  • Metadata-Driven Policy Management – Apply privacy rules dynamically.

  • Schema Evolution & Data Validation – Ensure compliance with privacy models (k-anonymity, l-diversity, t-closeness).

  • Version Control & Data Snapshots – Maintain auditability and reproducibility.

Why DataHub? DataHub ensures secure and controlled access to structured and unstructured data while maintaining full privacy compliance.

— ### 🔐 Local Authentication & Role-Based Access Control (RBAC) PAMOLA leverages a local Keycloak server for managing user authentication and fine-grained access control.

Keycloak’s role in PAMOLA:

  • Multi-Factor Authentication (MFA) for secure logins.

  • Role-Based Access Control (RBAC) for granular permission management.

  • OAuth 2.0, OpenID Connect, and SAML support for external integrations.

  • Session-Based Security to prevent unauthorized data access.

🔒 Keycloak ensures that only authorized users can modify datasets, execute privacy transformations, or access sensitive workflows.

— ### 🔄 Project & Pipeline System PAMOLA is structured around projects and privacy-enhancing pipelines, enabling flexible and modular AI-driven workflows.

Key Features:

PAMOLA offers a robust privacy-first architecture, enabling structured privacy management.

  • Projects define workflows, datasets, and privacy policies.

  • Pipelines automate data anonymization, risk modeling, and synthetic data generation.

Modular pipeline stages include:

  • Data Profiling & Risk Assessment

  • Anonymization & Masking

  • Synthetic Data Generation

  • Privacy Evaluation & Attack Simulation

  • Federated Learning & Secure Computation

Pipelines allow data engineers and privacy officers to implement and test privacy-enhancing transformations without direct exposure to raw data.

— ### 🛠️ PAMOLA CORE: High-Performance Privacy Engine At the heart of PAMOLA is PAMOLA CORE, an independent high-performance Python library for executing privacy-related computations.

PAMOLA CORE Capabilities:

  • Anonymization Processing – Implements k-anonymity, l-diversity, t-closeness.

  • Synthetic Data Generation – Based on PATE-GAN, DP-GAN, and Renyi Differential Privacy.

  • Federated Learning – Supports Horizontal & Vertical FL architectures.

  • Confidential Computing & Secure MPC – Enables Zero-Knowledge Proofs (ZKP), Private Set Intersection (PSI), and Homomorphic Encryption (HE).

  • Attack Simulation & Risk Assessment – Evaluates privacy risks using real-world adversarial models.

PAMOLA CORE is fully modular – it can run independently of PAMOLA’s UI or integrate into external privacy workflows.

— ### 🔄 How the Components Work Together 1. DataHub provides dataset metadata tracking and privacy policies. 2. Keycloak secures access control and authentication. 3. Projects & Pipelines manage privacy-enhancing AI workflows. 4. PAMOLA CORE executes high-performance privacy transformations.

This structure ensures compliance with global privacy regulations (GDPR, CCPA, PIPEDA) while maintaining AI model utility and security.

— ### ** Next Steps** - To understand PAMOLA’s system requirements and deployment, see the [System Guide](system_guide.html). - To learn how to use PAMOLA in real-world applications, explore [Use Cases](use_cases.html). - For developers looking to extend PAMOLA’s capabilities, visit the [Developer Guide](developer_guide.html).