Privacy Impact Assessments and Tools

Privacy Impact Assessment (PIA / DPIA)

A Data Protection Impact Assessment (DPIA) is a process designed to help systematically analyze, identify, and minimize the data protection risks of a project.

When is it required?

• Large-scale processing of highly sensitive data.
• Systematic monitoring of publicly accessible areas.
• Processing using new technologies (e.g., AI/ML).

LINDDUN Threat Modeling

LINDDUN is a privacy threat modeling methodology (similar to STRIDE for security). It stands for:

• Linkability
• Identifiability
• Non-repudiation
• Detectability
• Disclosure of information
• Unawareness
• Non-compliance

Applying LINDDUN

1. Model the System: Create Data Flow Diagrams (DFDs).
2. Map Threats: Apply the LINDDUN categories to the DFD elements (Entities, Data Flows, Data Stores, Processes).
3. Analyze: Assess the risk and impact of each threat.
4. Mitigate: Apply privacy-enhancing technologies and controls.

Privacy Tooling Ecosystem

• Google Differential Privacy Library: C++, Java, and Go libraries for generating differentially private statistics.
• Diffprivlib: IBM's Differential Privacy Library for Python, extending scikit-learn.
• OpenMined / PySyft: A Python library for secure, private Deep Learning (Federated Learning, SMPC, DP).
• ARX Data Anonymization Tool: An open-source tool for anonymizing sensitive personal data using models like k-anonymity.