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Method to assess identifiability in electronic data files.

Holly L Howe1, Andrew J Lake, Tiefu Shen

  • 1North American Association of Central Cancer Registries, Inc., Springfield, IL 62704-6495, USA. hhowe@naaccr.org

American Journal of Epidemiology
|December 22, 2006
PubMed
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The Record Uniqueness (RU) software quantifies confidentiality breach risks in electronic health databases. It assesses data uniqueness to protect patient privacy while enabling research data sharing.

Area of Science:

  • Health Informatics
  • Data Science
  • Biostatistics

Background:

  • Electronic health databases contain sensitive patient information.
  • Assessing and mitigating confidentiality breach risks is crucial for data sharing and research.
  • Existing methods may not adequately quantify re-identification risks from combined variables.

Purpose of the Study:

  • To introduce the Record Uniqueness (RU) software program for assessing confidentiality breach risks in electronic data.
  • To demonstrate RU's utility in evaluating the risk of adding socioeconomic measures to cancer registry data.
  • To highlight RU's role in balancing data confidentiality with research accessibility.

Main Methods:

  • The RU program generates frequency distributions for selected variables and their combinations.

Related Experiment Videos

  • It calculates regression coefficients to determine each variable's contribution to record uniqueness.
  • The method was applied to a large North American cancer registry dataset (4.67 million cases, 1995-2001).

    • Main Results:

    • RU successfully evaluated the confidentiality risk associated with adding county-based socioeconomic data.
    • The software quantifies the risk of re-identifiability through data triangulation or linkage.
    • The methodology provides assurance of record confidentiality for data release.

    Conclusions:

    • The Record Uniqueness (RU) software is a valuable tool for quantifying confidentiality breach risks in electronic health databases.
    • RU offers flexibility in adjusting uniqueness thresholds for various data release scenarios.
    • It supports responsible data stewardship, maximizing research utility while safeguarding patient privacy.