Reviewing Reidentification Risk

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

pythonCopy code
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.
deidentified = [
    {"record_id": "r1", "text": "[NAME], 47F, ZIP 021xx, admitted 2024-03."},
    {"record_id": "r2", "text": "[NAME], 47F, ZIP 021xx, admitted 2024-03."},
    {"record_id": "r3", "text": "[NAME], 88M, ZIP 597xx, admitted 2024-03."},  # singleton
]
# Auxiliary = what an attacker might already hold (e.g. a voter list).
auxiliary = [{"record_id": "v9", "text": "88M ZIP 597xx"}]

result = run_reid_attack(
    fixtures=[],                          # bring your own records below
    deidentified_records=deidentified,
    auxiliary_records=auxiliary,
)
metric = result.to_metric()
print(metric["aux_linkage_rate"],        # empirical linkage success
      metric["k_min"],                   # smallest equivalence-class size
      metric["singleton_count"],         # k=1 records (uniquely identifiable)
      metric["quasi_identifier_count"])

k_min is the population k-anonymity floor across the dataset; a k_min of 1 means at least one record is unique on its quasi-identifiers and is the highest re-identification risk. aux_linkage_rate is the empirical attack: how often the adversary's auxiliary data successfully links back to a released record.

To run against the bundled golden suite and emit a leaderboard-style report:

pythonCopy code
report = run_reid_benchmark(
    suite="golden",
    deidentified_records=deidentified,
    auxiliary_records=auxiliary,
    output_markdown="reid_risk.md",
)

Workflow

1. Enumerate quasi-identifiers (QIs). List every field an outsider could plausibly know and cross-reference: age/DOB, ZIP/region, dates of service, sex, race, rare conditions, provider. Direct identifiers should already be gone (verified by auditing-deid-leakage); QIs are what's left to worry about.
2. Compute k-anonymity. For each equivalence class (records sharing the same QI combination), the class size is k. run_reid_attack returns k_min and the list of singleton_records (k=1). A common Expert Determination target is k ≥ a documented threshold (e.g. k ≥ 5 or k ≥ 11) for every record.
3. Check l-diversity on sensitive attributes. k-anonymity hides which record, but if every record in a class shares the same sensitive value (e.g. all HIV-positive), the attribute leaks anyway. Require ≥ l distinct sensitive values per class; flag homogeneous classes.
4. Run the empirical attack. run_reid_attack / run_reid_benchmark model an adversary with auxiliary_records and measure actual linkage success (aux_linkage_rate), residual leakage (leakage_rate), surrogate-consistency leaks, and date-shift-inversion leaks. Structural metrics bound risk; the attack demonstrates it.
5. Generalize or suppress, then re-score. For singletons / low-k classes, coarsen QIs (age → age band, ZIP5 → ZIP3, exact date → month/quarter) or suppress the record, then re-run until k_min and linkage rate meet your documented threshold.
6. Write the determination memo. Record the QIs considered, methods applied, k_min, l-diversity, the attack's aux_linkage_rate, the assumptions about attacker capability, and the conclusion that residual risk is "very small." Cite the metrics — never paste raw records into the memo.

Hand-off to / from OpenMed

• From auditing-deid-leakage: only score QI risk once direct-identifier leakage is zero. A leak short-circuits the whole determination.
• OpenMed calls: from openmed.eval.attacks.reid import run_reid_attack, run_reid_benchmark, generate_reid_leaderboard. The attack delegates to openmed.risk.risk_report for k-anonymity / linkage internals.
• To evaluating-with-leakage-gates: register reid_leakage_rate as a gate in the eval harness so re-identification risk regressions fail CI.
• From pseudonymizing-for-gdpr: pseudonymized output is still re-identifiable via QIs — run this attack before claiming a dataset is low-risk or anonymized.

Edge cases & gotchas

• Expert Determination is a human judgment. OpenMed produces the statistics; a qualified expert signs the determination. The tool supports the memo, it is not the memo.
• Auxiliary data assumptions drive the result. Linkage rate is only as meaningful as the auxiliary_records you model. Document the assumed attacker (motivated insider vs. public voter list) — different aux sets, different risk.
• Singletons are the headline. A single k=1 record can sink a release; check singleton_count and singleton_records first.
• Date-shift can be inverted. Preserving intervals across a date shift lets an attacker re-anchor the timeline; the attack flags date_shift_inversion_rate. Watch it when de-id used method="shift_dates".
• No raw records in artifacts. Reports carry counts, rates, and offsets. Keep the underlying dataset out of the memo and out of logs.
• Local-first. Run the attack on-device; never ship candidate-release data to a third party to "test" re-identifiability.

Standards & references

• HIPAA Expert Determination — 45 CFR 164.514(b)(1): https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/#expert-determination
• Sweeney, k-anonymity: a model for protecting privacy (2002): https://dataprivacylab.org/dataprivacy/projects/kanonymity/
• Machanavajjhala et al., l-diversity (2007): https://dl.acm.org/doi/10.1145/1217299.1217302
• NIST SP 800-188, De-Identifying Government Data Sets: https://csrc.nist.gov/pubs/sp/800/188/final