<!-- Copyright (c) 2026 defconxt. All rights reserved. -->
<!-- Licensed under AGPL-3.0 — see LICENSE file for details. -->
<!-- NERF is a trademark of defconxt. -->
---
name: performing-linddun-privacy-threat-modeling
description: >-
  Apply LINDDUN privacy threat modeling to systematically identify Linkability, Identifiability, Non-repudiation, Detectability, Disclosure, Unawareness, and Non-compliance threats in data processing systems.
domain: cybersecurity
subdomain: threat-modeling
tags:
  - linddun
  - privacy
  - gdpr
  - data-protection
  - pii
  - dpia
  - privacy-by-design
version: "1.0"
author: defconxt
license: AGPL-3.0
metadata:
  mitre-attack: ["T1557", "T1040", "T1565", "T1530"]
---

# Performing LINDDUN Privacy Threat Modeling

## Overview

LINDDUN is a privacy-specific threat modeling framework that identifies seven categories of
privacy threats in data processing systems. This technique covers LINDDUN analysis mapped to
GDPR articles, privacy-enhancing technology recommendations, and DPIA integration.

Mode: `[MODE: PRIVACY]` — Privacy threat identification and data protection analysis.

## Prerequisites

- Data processing inventory and data flow documentation
- Understanding of applicable privacy regulations (GDPR, CCPA, HIPAA)
- Knowledge of data subjects and processing purposes
- Python 3.10+

## Key Concepts

### LINDDUN Categories

| Threat | Privacy Property | GDPR Article | Example |
|--------|-----------------|--------------|---------|
| **L**inkability | Unlinkability | Art. 5(1)(b) | Correlating user actions across services |
| **I**dentifiability | Anonymity | Art. 5(1)(c) | Re-identifying anonymized data |
| **N**on-repudiation | Plausible deniability | Art. 17 | Inability to deny an action |
| **D**etectability | Undetectability | Art. 5(1)(f) | Revealing existence of data |
| **D**isclosure | Confidentiality | Art. 5(1)(f) | Unauthorized access to personal data |
| **U**nawareness | Content awareness | Art. 13/14 | Users unaware of data collection |
| **N**on-compliance | Policy compliance | Art. 5/6/7 | Processing without legal basis |

### Privacy-Enhancing Technologies (PETs)

| PET | Addresses | Use Case |
|-----|-----------|----------|
| Differential Privacy | Linkability, Identifiability | Analytics without individual exposure |
| Homomorphic Encryption | Disclosure | Computing on encrypted data |
| Zero-Knowledge Proofs | Identifiability | Proving attributes without revealing data |
| K-Anonymity | Identifiability | Ensuring k identical records |
| Data Minimization | All categories | Collecting only necessary data |
| Purpose Limitation | Non-compliance | Restricting use to stated purposes |

## Workflow

### Step 1: Map Data Processing Activities

```bash
node scripts/agent.js --action map-processing --input data-flows.json --output processing.json
```

### Step 2: Enumerate LINDDUN Threats

```bash
node scripts/agent.js --action enumerate --input processing.json --output threats.json
```

### Step 3: Assess Privacy Impact

```bash
node scripts/agent.js --action assess --input threats.json --output dpia.json
```

### Step 4: Recommend PETs and Controls

```bash
node scripts/agent.js --action recommend --input dpia.json --output recommendations.json
```

## Verification

- [ ] All data processing activities mapped with legal basis
- [ ] LINDDUN applied to every data flow involving personal data
- [ ] Privacy threats mapped to applicable GDPR articles
- [ ] DPIA completed for high-risk processing activities
- [ ] Privacy-enhancing technologies recommended for each threat category
- [ ] Data subject rights (access, erasure, portability) addressable

## References

- [LINDDUN Privacy Threat Modeling](https://linddun.org/)
- [GDPR Full Text](https://gdpr-info.eu/)
- [OWASP Privacy Risks](https://owasp.org/www-project-top-10-privacy-risks/)
- [NIST Privacy Framework](https://www.nist.gov/privacy-framework)
