identifying
Installation
SKILL.md
Identifiability Analysis (LINDDUN I)
Analyze source code for identifiability threats where individuals can be identified from supposedly anonymous data. Combinations of quasi-identifiers (zip code, birth date, gender) can uniquely identify individuals. Re-identification attacks on "anonymized" data are the primary concern.
Supported Flags
Read ../../shared/schemas/flags.md for full flag
documentation. This skill supports all cross-cutting flags.
| Flag | Identifiability-Specific Behavior |
|---|---|
--scope |
Default changed. Focuses on files handling user data, anonymization logic, data exports, analytics pipelines, and API responses. |
--depth quick |
Grep patterns only: scan for PII in logs, quasi-identifiers in exports, and missing anonymization. |
--depth standard |
Full code read, analyze data fields returned in APIs and stored in databases for re-identification risk. |
--depth deep |
Trace data flows from collection to storage to export. Assess quasi-identifier combinations across the system. |
--depth expert |
Deep + re-identification risk modeling: estimate k-anonymity violations and uniqueness of attribute combinations. |
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