reviewing-reidentification-risk

reviewing-reidentification-risk is a Privacy & regulatory compliance skill for AI agents, published by maziyarpanahi in the openmed repository on GitHub. Skillspace rates reviewing-reidentification-risk C (74/100) and it passed the safety scan; the rating is based on static analysis only.

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 adversarial attack, and writing the risk memo. Pairs after deidentifying-clinical-text and auditing-deid-leakage.

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

5,313

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