---
title: "LINDDUN"
type: "entity"
entity_kind: "concept"
status: "hub"
canonical_name: "LINDDUN"
aliases: []
first_seen: "2026-10-20T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["privacy threat modeling","mHealth","consent","GDPR","PET (privacy-enhancing technologies)"]
seek_code_commit: "d40ee39"
---


LINDDUN is a privacy threat-modeling framework (Deng, Wuyts, Scandariato, Preneel & Joosen, 2011) that gives privacy engineers a structured way to enumerate the ways a system can fail its users on privacy, named for its seven threat categories: Linkability, Identifiability, Non-repudiation, Detectability, Disclosure of information, Unawareness, and Non-compliance.

In this vault LINDDUN is, so far, the categorical ancestor behind Myers, De Carli, Sharma, Banerjee, Reardon & Ray's "Bad Diagnosis" (arXiv:2610.23851, 2026), which explicitly maps its own three measured privacy-threat types — permission overreach, absence of consent, abuse of consent — onto the LINDDUN taxonomy. The vault has not yet read LINDDUN's own 2011 paper as a primary source; this hub exists because the framework is load-bearing to the three claims below, not because the vault has independently verified its content.

## References
- [[claim-mhealth-apps-69pct-request-unjustified-android-permissions]]
- [[claim-mhealth-apps-93pct-show-no-consent-mechanism-on-first-launch]]
- [[claim-mhealth-consent-forms-7pct-collect-undisclosed-pii]]
- Captures: 10-inbox/raw/2026-10-20-outside-arxiv261023851-bad-diagnosis-analyzing-consent-and-data.md