Health Data Anonymization Test

Comprehensive privacy protection and re-identification risk assessment based on HIPAA Safe Harbor method, Expert Determination standards, k-anonymity model, l-diversity principle, and GDPR Article 89 safeguards.

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Critical Disclaimer: Educational Use Only

This privacy assessment tool is for educational, research planning, and preliminary evaluation purposes only. It does NOT constitute: legal advice, formal HIPAA compliance certification, IRB approval, or professional privacy expert determination.Re-identification risk assessment requires qualified privacy experts (statisticians, health informaticists, legal counsel) with deep knowledge of your specific dataset, population, and use case. This tool provides general guidance based on established privacy frameworks but cannot replace comprehensive privacy impact assessments or formal Expert Determination per 45 CFR §164.514(b)(1).Do not share sensitive health data based solely on this assessment. Engage qualified professionals before any data sharing or publication.

Assessment Methodology

Framework Basis

This assessment integrates multiple evidence-based privacy frameworks:

HIPAA Safe Harbor: Requires removal of 18 specific identifier categories (45 CFR §164.514(b)(2))
HIPAA Expert Determination: Statistical/scientific principles to assess very low re-identification risk
k-anonymity: Each quasi-identifier combination must appear for ≥k individuals (Sweeney 2002)
l-diversity: Each k-anonymous group must have ≥l distinct sensitive attribute values
t-closeness: Sensitive attribute distributions within groups match overall distribution
GDPR Article 89: Safeguards for research data including pseudonymization and minimization

Scoring System

Weighted scoring across 6 privacy dimensions:

Direct Identifiers (35%): Removal of names, SSN, contact info, biometrics
Quasi-Identifiers (25%): De-identification of dates, geography, rare conditions, age extremes
k-Anonymity (20%): Group size and quasi-identifier generalization
l-Diversity (10%): Sensitive attribute diversity and t-closeness
External Linkage (5%): Public dataset availability and differential privacy
Documentation (5%): Process documentation and expert review

Note: Higher privacy scores indicate lower re-identification risk. Risk Score = 100 - Privacy Score.

Interpretation Guidelines

0-20% Risk (Low): Strong de-identification, likely HIPAA compliant, suitable for research sharing with DUA
21-40% Risk (Moderate): Acceptable with additional safeguards; expert determination recommended
41-60% Risk (High): Substantial risk, insufficient for compliance; significant additional protection needed
61-100% Risk (Critical): Severe privacy violations; dataset should not be shared externally

Each question includes methodology notes explaining the privacy science basis and regulatory requirements.

Health Data Anonymization Test

Critical Disclaimer: Educational Use Only

Assessment Methodology

Framework Basis

Scoring System

Interpretation Guidelines

1. Are patient names completely removed or irreversibly pseudonymized?

2. How are Social Security Numbers, Medical Record Numbers, or National IDs handled?

3. Are email addresses, phone numbers, and URLs removed?

4. Are biometric identifiers (facial images, fingerprints, voice) removed or de-identified?

5. How are dates (birth date, admission/discharge dates, death date) handled?

6. How is geographic information (address, ZIP code) de-identified?

7. Are rare diagnoses, procedures, or genetic variants suppressed or generalized?

8. How is age handled for very young (<1 year) or very old (>89) individuals?

9. What is the minimum equivalence class size (k-anonymity level) in your dataset?

10. Have you verified k-anonymity across all critical quasi-identifier combinations?

11. What is the diversity of sensitive attributes within each equivalence class?

12. Is t-closeness assessed for sensitive attribute distributions?

13. Are there publicly available datasets that could be linked to re-identify individuals?

14. Has differential privacy or noise injection been applied?

15. Is there comprehensive documentation of all de-identification steps and residual risks?

16. Has an independent expert reviewed the de-identification and assessed re-identification risk?