testland/k-anonymity-verifier
Verifies that a masked dataset satisfies k-anonymity, l-diversity, and t-closeness by computing equivalence classes over chosen quasi-identifiers and reporting re-identification risk. Covers quasi-identifier selection heuristics, threshold guidance, pycanon API (k_anonymity / l_diversity / t_closeness / report), ARX Java API and GUI workflow, SmartNoise for differential-privacy comparison, and CI-gate integration. Distinct from data-masking-techniques-reference (which catalogs masking operators but defers k-anonymity measurement to dedicated tooling) and from presidio-pii-detection (which detects PII spans but offers no equivalence-class analysis). Use when you need to confirm whether a masked dataset meets a stated k, l, or t threshold before promoting it to a non-production environment.
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ci-gate.mdreferences/
k-anonymity CI gate
Referenced from SKILL.md Step 6. Block promotion of a masked
dataset unless it meets the agreed thresholds recorded in qi-policy.yaml.
The script loads the policy, computes k / l / t with pycanon, and exits
non-zero on any breach so a failing dataset cannot be promoted.
# scripts/k_anonymity_gate.py
import sys, json
import pandas as pd
from pycanon import anonymity, report
data = pd.read_csv(sys.argv[1])
policy = json.load(open("qi-policy.yaml".replace(".yaml", ".json")))
QI = policy["quasi_identifiers"]
SA = policy["sensitive_attributes"]
k_min = policy["thresholds"]["k_min"]
l_min = policy["thresholds"]["l_min"]
t_max = policy["thresholds"]["t_max"]
k = anonymity.k_anonymity(data, QI)
l = anonymity.l_diversity(data, QI, SA)
t = anonymity.t_closeness(data, QI, SA)
failures = []
if k < k_min:
failures.append(f"k={k} < required {k_min}")
if l < l_min:
failures.append(f"l={l} < required {l_min}")
if t > t_max:
failures.append(f"t={t:.4f} > allowed {t_max}")
if failures:
print("PRIVACY GATE FAILED:")
for f in failures:
print(f" {f}")
sys.exit(1)
print(f"PASS k={k} l={l} t={t:.4f}")# .github/workflows/privacy-gate.yml
name: privacy-gate
on: pull_request
jobs:
verify:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v5
- uses: actions/setup-python@v6
with: { python-version: '3.12' }
- run: pip install pycanon
- run: python scripts/k_anonymity_gate.py masked_dataset.csvThe pycanon functions anonymity.k_anonymity, anonymity.l_diversity, and
anonymity.t_closeness used here are documented at
github.com/IFCA-Advanced-Computing/pycanon.