reviewing-reidentification-risk
Run expert-determination-style quasi-identifier risk scoring (k-anonymity, l-diversity) plus OpenMed's empirical re-identification attack on a de-identified dataset, then document residual risk in a defensible memo. Use when the user needs HIPAA Expert Determination (45 CFR 164.514(b)(1)) support, asks whether a dataset is safe to release, worries about singling-out via age/ZIP/dates, or wants a statistical \"very small risk\" determination. Covers identifying quasi-identifiers, computing k-anonymity / l-diversity, running openmed.eval.attacks.reid (run_reid_attack / run_reid_benchmark) as the
Profile →npx skills add maziyarpanahi/openmed --skill reviewing-reidentification-risk --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Reviewing re-identification risk Removing direct identifiers is not enough. A record stripped of name, SSN, and MRN can still be **singled out** by a combination of *quasi-identifiers* — age, ZIP/region, admission date, sex, rare diagnosis. The HIPAA Expert Determination pathway (45 CFR 164.514(b)(1)) requires a qualified person to apply statistical methods and document that the risk of re-identification is **"very small."** This skill produces that evidence: quasi-identifier risk metrics (k-anonymity, l-diversity) plus OpenMed's empirical re-identification attack, written up as a residual-risk memo. ## When to use - After direct-identifier removal passes `auditing-deid-leakage` (no leaks) and you must decide whether the dataset is releasable. - The user invokes Expert Determination, asks for a re-identification risk score, k-anonymity, l-diversity, or a "very small risk" determination memo. - You need an adversarial linkage attack — modeling an attacker with auxiliary data — not just a structural metric. ## Quick start ```python from openmed.eval.attacks.reid import run_reid_attack, run_reid_benchmark # Synthetic de-identified records; each row is the released, de-id'd data. dei
- When to use
- Quick start
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
What does the reviewing-reidentification-risk skill do?
Run expert-determination-style quasi-identifier risk scoring (k-anonymity, l-diversity) plus OpenMed's empirical re-identification attack on a de-identified dataset, then document residual risk in a defensible memo. Use when the user needs HIPAA Expert Determination (45 CFR 164.514(b)(1)) support, asks whether a dataset is safe to release, worries about singling-out via age/ZIP/dates, or wants a statistical \"very small risk\" determination. Covers identifying quasi-identifiers, computing k-anonymity / l-diversity, running openmed.eval.attacks.reid (run_reid_attack / run_reid_benchmark) as the
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill reviewing-reidentification-risk --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.