red
Residual re-identification RISK CHECK on text you have ALREADY redacted (defensive, dual-use). Use when the user asks to "check residual re-id risk", "red-team my redaction", "what can an attacker still infer", "is this safe to share", or assess "re-identification risk" after anonymizing. Re-runs the CONFIDE detectors on the redacted output to surface surviving identifiers (singling-out), checks multiple files for linkability, and optionally probes a local model for still-inferable attribute CATEGORIES (inference) — mapped to GDPR Art-29. Reports risk categories/counts only, never a re-identification recipe. Pairs with confide:anon (run AFTER redacting).
What this skill does
# confide:red — residual re-identification risk check A **defensive** audit of YOUR OWN already-redacted output. It does not score against ground truth and is not a benchmark. It surfaces, qualitatively, what an attacker could **still** do — mapped to GDPR Art-29: **singling-out**, **linkability**, **inference**. ## GUARDRAILS — read before running - Run **only on the user's own redacted output**. If asked to de-anonymize or re-identify third-party / non-consented data, **refuse**. - Report risk **categories and counts only** — never produce a step-by-step re-identification recipe or guess the hidden values. - **Local attacker by default.** Enable the cloud/LLM inference probe (`--inference`) **only** on synthetic or explicitly consented data. - **Absence of a finding ≠ safety.** A weak local detector/attacker is a FLOOR, not a ceiling. Always tell the user **human review is still required**. - This pairs with **confide:anon** — run `red` *after* redacting, on the redacted file. ## What it checks 1. **Singling-out (deterministic, offline — the load-bearing signal):** re-run `detect_regex` (+ `detect_natasha` if available) on the **redacted** text. Anything they still find is a **surviving identifier the redaction missed**. Counts by type. 2. **Linkability (multi-file):** given a folder, compare every file pair for shared surviving quasi-identifiers and flag potentially linkable pairs (count + types only). 3. **Inference (LLM, optional, opt-in):** prompt the local attacker model (`cfg.red_attacker_model`) for the attribute **categories** it could still infer (profession, location type, age band, …). Degrades gracefully if no model. WARN the user it under-reports (floor, not ceiling). ## Risk tier rule - **HIGH** — any DIRECT identifier survives (EMAIL, PHONE, URL, ID, PERSON). - **MEDIUM** — only QUASI identifiers survive (LOCATION, ORG, DATE, AGE, PROFESSION, MEDICATION), or linkable pairs exist across files. - **LOW** — no surviving identifiers found (still NOT a guarantee). ## How to run ```bash # single redacted file (offline, deterministic) python3 skills/red/scripts/red.py path/to/file.green.md # a folder of redacted files (adds linkability) python3 skills/red/scripts/red.py path/to/redacted_dir/ # add the local inference probe — synthetic/consented data ONLY python3 skills/red/scripts/red.py path/to/file.green.md --inference # machine-readable python3 skills/red/scripts/red.py path/to/file.green.md --json ``` ## Output A residual-risk report: per-file surviving-identifier **counts by type**, an overall **risk tier**, the inference **categories** claimed (if probed), the **linkable-pair count**, and the caveat that *absence of a finding ≠ safety; human review still required*. No PII values, no re-identification steps.
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