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Re-Identification Risk in HIPAA De-Identified Datasets: The MVA Attack.

Victor Janmey1, Peter L Elkin1

  • 1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
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Motor vehicle accident (MVA) data in patient records poses a high re-identification risk, even when de-identified using HIPAA Safe Harbor standards. Omitting MVA location details is crucial for protecting patient privacy against such attacks.

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Area of Science:

  • Health Informatics
  • Data Privacy
  • Medical Record Security

Background:

  • HIPAA Safe Harbor de-identification is intended to protect patient privacy.
  • Certain data points, like motor vehicle accident (MVA) details, may inadvertently increase re-identification risks.
  • Indirect identifiers can compromise de-identified health records.

Purpose of the Study:

  • To assess the re-identification risk associated with de-identified patient records documenting motor vehicle accidents (MVAs).
  • To evaluate the effectiveness of HIPAA Safe Harbor de-identification against indirect identifier attacks.
  • To propose protective measures for sensitive MVA data in clinical notes.

Main Methods:

  • Analysis of de-identified patient records using indirect identifiers.
  • Quantification of re-identification risk for patients involved in MVAs.
  • Comparative risk assessment against the general population.

Main Results:

  • A significant re-identification risk was identified for records documenting MVAs.
  • The relative risk of re-identification for MVA cases was 537 compared to the general population.
  • Documentation of MVAs, particularly severe ones, presents a substantial privacy vulnerability.

Conclusions:

  • Patient records documenting MVAs are vulnerable to re-identification attacks, even after HIPAA Safe Harbor de-identification.
  • Omitting MVA location information is recommended to mitigate re-identification risks.
  • Clinicians should exercise caution with MVA data to enhance patient privacy protection.